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Article

Technology Challenges and Aids: The Sustainable Development of Professional Interpreters in Listening Comprehension Effectiveness and Interpreting Performance

by
Yuwei Huang
1,*,
Weinan Shi
2 and
Jinglin Wen
3
1
School of Foreign Studies, University of Science & Technology Beijing, Beijing 100081, China
2
International Office of BJUT, Beijing University of Technology, Beijing 100081, China
3
Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6828; https://doi.org/10.3390/su15086828
Submission received: 15 March 2023 / Revised: 14 April 2023 / Accepted: 14 April 2023 / Published: 18 April 2023
(This article belongs to the Section Sustainable Education and Approaches)
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Abstract

:
Technology plays a double-edged role in the interpreting job market. The development of technology may threaten the jobs of interpreters, as well as provide aids for them. The sustainable development of interpreters depends on determining the balance between the challenges and aids of modern technology so as to take advantage of it to enhance the performance of interpreting to fend off the challenge of technology itself. This paper launches these empirical studies by taking a tech product (a speech recognition device) as the study object, considering the lack of empirical studies about technology-assisted interpreting, as well as to explore the way and the extent to which the technology can facilitate interpreting performance in such a competitive world. Listening comprehension is generally regarded as the most difficult element in interpreting because understanding what we hear requires a colossal amount of cognitive effort, which will inevitably jeopardize the overall performance of interpreting. Therefore, how and to what extent the application of technology is capable of improving this performance is worth our attention. To measure the interpreting performance via the domain of language accuracy, message fidelity, etc., the empirical research conducted in the paper includes 30 student interpreters and 20 professional interpreters to explore the extent to which speech recognition assistance can increase the effectiveness of interpreting, as well as to shed some light on the professional sustainability and future training of interpreters.

1. Introduction

Interpreting is never an easy job for any interpreter in the job market [1,2,3] in that interpreters have to handle listening (input and decoding), language conversion, memorizing, and speaking (output and encoding) synchronously. Even in consecutive interpreting, interpreters have multi-task; therefore, the “cognitive burden” is also extremely high. Reducing the “burden” seems to be a key item on researchers’ to-do lists [4,5]. The professionalism of interpreters is changing from time to time; with the enhancement of modern technology in the contemporary world, the training of interpreters needs to apply modern technology to consolidate the interpreting performance and reduce the cognitive pressure. This will help interpreters to increase professional sustainability among the modern challenges, as some experts predict that technology products will replace human interpreters in the foreseeable years. In all interpreting procedures, listening serves as the key obstacle that haunts the majority of interpreters; therefore, solving the problem of listening comprehension in interpreting would help improve the sustainability of the interpreting career, since new challenges keep popping up in the modern era.
Listening capacity is the first key stage for interpreters, since processing speech information requires several steps, including input, decoding, and transferring into note tokens in the brain [6], which is the traditional consecutive interpreting module. Multi-tasking incurs the loss of some input, the lack of time to interpret certain terminology, or the length of time to convert the message from acoustic input to notes [7], which all lead to the decline of the overall interpreting performance. The traditional consecutive interpreting model stipulates that interpreters decode the listening input while converting the information into note symbols and inputting those symbols into an order system, etc. Moreover, interpreters have to view the notes they write down and process them into logical spoken discourse (encoding in language utterance) simultaneously so as to deliver the accurate message to a certain recipient. This process is demonstrated in Figure 1:
Needless to say, reducing the processing effort is critical in improving the cognitive efficiency and interpreting performance. Note-taking and conversion between speech sound and notes’ tokens are eliminated by the introduction of speech recognition tools, which further diminishes the cognitive burden of voice decoding and accurate and complete message recording. Compared to the traditional consecutive interpreting model, interpreters would no longer need to take notes while looking at the screen showing speech recognition tools (such as some speech recognition Apps) and listening to speakers, thus taking advantage of what is demonstrated on the screen to turn interpreting into sight interpreting. This reduces the cognitive effort of converting symbols in notes to utterance by directly watching the sentences on the screen and producing utterance, as demonstrated in Figure 2:
In recent years, many experts have studied from the perspective of cognition [8,9] because discovering a way to reduce the cognitive burden of interpreters is their key purpose. Therefore, determining whether using modern technology can help reduce the cognitive burden and improve the sustainability of interpreters in the job market is worth our attention, since plenty of fields are employing modern technology to enhance their productivity, make up for their flaws, and meet the market need.

2. Literature Review

According to the survey of Wang Xiangyu, 60% of all interpreting services were required by government agencies or other institutions, and most of the respondents in the survey agreed that the period between May to November was the peak season for interpreting services. In addition, the survey found that the average demand for interpreting services every month was around 1 h to 30 h [10]. The market of interpreting services requires the consistent standard of interpreters and interpreter training. The great importance of having regulated standards, norms, and ethics codes for the professionalization of interpreting emphasizes the operations of qualification, certification, and following-up training for interpreters. The Chinese scholar Bao and his essay serves as a guideline for researchers and scholars of translators and interpreters who are interested in examining the degree of professionalization of interpreting [10]. Given the ever-changing nature of the market, surveys on market demands in the past may become obsolete, and thus, their timely updating is necessary. Adapting interpreters and the training of interpreters to the ever-changing market need is essential [11,12,13,14,15,16,17]. Based on previous studies on job analysis, the current survey of market demands (qualifications, interpreting modes and directions, interpreter competences, and so on) has implications for curriculum developers, instructors, students in interpreting, and professional interpreters. As of March 2018, 249 universities in China have been authorized to offer MTI (Master of Translation and Interpreting) programs, according to the National Committee for Translation and Interpreting Education [10]. Research has shown that one problem of current MTI education is the lack of standardization of the actual translation and interpreting market requirements, resulting in a gap between MTI education and translation and interpreting market needs, which surely affects graduates’ employability [18].

2.1. The Sustainability of Interpreters in Changing Market Need

Generally speaking, the job market keeps changing for interpreters due to the challenge of the modern technology. The sustainable development of interpreters in the job market depends on their ability, experience, skills etc., to meet the market needs. On top of that, the market values work experience, since the majority of companies need the interpreters to fit in job posts as soon as possible, which requires certain amount of experience in the job market. Secondly, most of the student interpreters taking the first step into the job market, with little working experience, ought to take certain aptitude tests to prove their capability to interpret. Honestly, the interpreting certificates, such as CATTI (China Accreditation Test for Translators and Interpreters) and SIA—Advanced (Shanghai Interpreting Accreditation), are not easily obtained in China. Finally, the job market still takes into consideration the majors and educational degrees of the candidates, if the first and second requirements are not met. The details are shown in Figure 3:
Generally, universities, which provide degree and help students to acquire certain certificates, are the key locations for training interpreters. Furthermore, work experience is also needed for the job market; therefore, students can get more working experience if the universities cooperate with some other companies throughout the school years. Thus, the following question arises: Is what universities teach able to fulfill the needs of the market? Or is there anything that we need to add to university training?
The market also requires the training of interpreters to meet its changing needs. The traditional training of interpreters remains committed to following aspect: (1) language competence training, (2) modes of interpreting, (3) events for interpreting, (4) fields for interpreting etc. Among these, language competence tops them all, in that the language competence [19] is the paramount capability for students to acquire. What students learn to acquire most is the language competence, which is also the teaching focus of the majority of the instructors in class [20,21,22,23].
Usually, language competence training includes the following elements, as shown in Figure 4:
However, a challenge is evolving with the development of modern technology, which is never taught in the training classes. In other words, taking advantage of modern technology and embedding it in the training will shed light upon the sustainable development of interpreters, as well as consolidate interpreters’ performance to fend off their replacement with AI interpreting technology, i.e., doctors use modern technology to help cure patients, rather than relying solely on use of experience to diagnose patients, as was the case hundreds of years ago.
What intrigues us is that the needs of market remain consistent, as shown in Figure 3: (1) years/times of interpreting experiences, (2) qualification certification obtained, (3) translation or interpreting related educational background, and (4) expertise of interpreting. As summarized above, the needs of the market are goal-oriented [24], and the market only takes certain indicators, such experience, degree, or certification, into account to make sure the interpreters they hire can do the job as expected, i.e., all employers need is to get the job done excellently without considering what approaches are adopted, since more terminologies are currently involved in the interpreting service, and the burden of interpreters cannot be taken carried solely by brain. In all, acquiring certain skills in using modern technology as the auxiliary role in interpreting service should be brought to the table [25,26,27,28].
As discussed above, the market only measures skill by the quality of the outcome, i.e., as long as you fulfil the need of employers, they seem not to pay any attention to the modern approaches you have adopted. Therefore, taking advantage of modern technology can serve the purpose and help interpreters to better perform, reducing the disadvantageous aspect of language competence, such as note-taking, listening decoding, memory, and so on. Some frequently used instruments are as follows:
From the Figure 5, it can be seen that unfortunately, search engines, electronic corpora, computer-assisted translation tools, etc., as instruments used in interpreting, currently play an inferior role in interpreting training. This echoes what was mentioned above, reaffirming that the training of interpreters still revolves around language competence, with less training regarding the use of instruments for improving performance or making up for the lack of language competence. In this study, we focus on listening comprehension in that it serves as the initial stage of the whole process, and it is also one of the biggest challenges in interpreting, as mentioned previously. Besides the intense tempering of the listening skills, taking advantage of modern technology to help to enhance the performance of interpreting is useful, if it is helpful. Furthermore, we explore how much modern technology can help improve the performance of interpreters so as to fend off the challenges of their professions and shed lights on the futuristic training of interpreters in the era of science and technology.

2.2. A Critical Review of Listening Processing and Its Cognitive Load

To the second language learners, the cognitive efforts of processing are surely higher for the second rather than the mother language [29]. The linguistic barriers (vocabulary, sentence patterns, etc.) are the main areas for increasing the listening cognitive efforts [30,31,32]. Furthermore, subjects of different levels require different emphasis in regards to L2 listening processing. High-level subjects possess higher listening comprehension levels than low-level subjects in overall processing; however, the low-level subjects place more attention upon partial cognitive processing [33]. This is because high-level subjects can judge the input from the perspective of the whole and “skip” the “entanglement” of local information by virtue of experience, but the low-level subjects are easily “entangled” by scattered “difficult input” and sever the smoothness of the processing, which will prompt the subjects to think about the “difficult input”. All this will only cause the subjects to fail to keep up and follow the input, leading to a decline in the level of listening comprehension. Holistic and partial processing are two significant approaches in cognitive processing, and subjects are more inclined to use global processing to grasp listening comprehension [34]. However, the holistic performance of interpreting will be impaired if the partial processing encounters even few difficulties. The listening comprehension will be utterly improved if the partial processing efforts can be reduced; therefore, interpreters will not be so susceptible to inadequate interpreting performance.
Plenty of experts have studied listening comprehension from the perspective of cognition; they generally focus on the “ cognitive model in listening comprehension” [35]; “decoding of phonetic information” [32]; “cognitive strategy of listening” [36], etc. All of them are committed to the ultimate goal of dissecting listening comprehension by virtue of cognitive studies. Nonetheless, besides the difficulties in vocabulary, syntax, and multi-sense loaded information (linguistic barriers) in the input of the interpreting process, the speed and accent of speakers impose a heavy load for understanding, and people speak faster than they can follow [37]; all of these non-linguistic barriers caused by speakers bring about the increase in the cognitive efforts of interpreters, which surely causes the decline of interpreting, in that interpreters have to sort out not only input (listening), but language conversion, memorizing, and output (speaking) as well.
According to Sweller, cognitive load is the working memory load of an individual in cognitive activities, which requires not only input, but also decoding, encoding, recording, and extraction [38]. The cognitive efforts do not only dwell on listening comprehension in consecutive interpreting, but upon language conversion, note conversion, note writing, decoding and encoding, etc. Interpreters are not as proficient in the foreign language as in the mother language, and voice recognition, syntactic parsing, and semantic acquisition etc., are supposed to occur in the first phase of interpreting, which requires a great amount of cognitive effort [39]; however, the cognitive efforts for decoding and retaining the input while listening will be drastically diminished if a visual aid is provided in sync. The mode of consecutive interpreting is now transformed into audio/sight interpretation.

2.3. Speech Recognition and Its Assistance in Interpreting

Speech recognition serves as a “cure” for reducing cognitive effort in listening comprehension for interpreters. According to previous studies, voice recognition technology began in 1952, when Davis et al., at the Bell Institute, successfully developed the world’s first experimental system that could recognize the pronunciation of 10 English numbers [40]. Denes et al. successfully produced the first computer speech recognition system in Britain in 1960. With the improvement and the increase in applying the artificial neural network (ANN) in speech recognition, the performance of speech recognition is constantly improving, and intelligent speech recognition has begun a rapid development mode, as it is applied to all walks of life (https://baike.baidu.com/item/%E8%AF%AD%E9%9F%B3%E8%AF%86%E5%88%AB%E6%8A%80%E6%9C%AF/5732447?fr=aladdin, accessed on 3 November 2022).
The modern intelligent speech recognition system begins with the speech input and converts it into an acoustic construct [41]; then, an “optimal result” is created after decoding and encoding under the screening system of an algorithm. Li Xiaolong, Wang Mengjie points out, in empirical studies, that the interpreting performance (completeness, term accuracy, meaning fidelity, synchrony, oral expression skills, voice quality) was improved after the Iflytek speech assistant intervened in interpreting [34]. The reason why speech recognition assistance is more popular with interpreters than are AI simultaneous interpretation is that interpreters cannot find the original text in time to correct it when they are aware of mistakes using AI interpretation, while speech recognition directly provides the original text from the initial stage of interpretation, so as to facilitate interpreters in supporting each other through their own listening comprehension and speech recognition. However, there are few studies on the validity of speech recognition assistance for different interpreters. The specific improvement of each population is also worth exploring.

3. The Participants, Methodology, and Experiments

In order to test how much the speech recognition assistance can help improve the interpreting performance and bridge the gap between interpreters in the job market, the following tests are launched. First and foremost, there are 50 subjects involved in tests. Among them, there were 30 student interpreters (the second-year postgraduates majoring in interpretation, with some experience and training in interpretation, but who had not passed the CATTI Interpretation Level 2 exam) and 20 professional interpreters (from a provincial interpretation association, all of whom had passed the CATTI Interpretation Level 2 exam, or above, and had been engaged in interpretation teaching and practice).
The significance of including student interpreters in the empirical studies is that student interpreters represent the low-level interpreters for the low-end market, since certain interpreting services do not require much interpreting capability. On the contrary, professional interpreters are dealing with a high-end market, such as international conference interpreting, and the challenge of high-end market are never off stage. Therefore, the efficacy of speech recognition tools can be verified in a wider spectrum of different levels of markets.
To the training of interpreters, students interpreters are the quasi-interpreters in the future professional market, and finding better training approaches for modern era is the key for them to become professional interpreters in an ever-challenging time. The sustainable development of professional interpreters is intimately connected with technology in the modern period, so either the technology will replace practitioners in this field, or practitioners will take technology as a weapon to take meet the current professional challenges. Therefore, the comparative studies between student and professional interpreters reflect the potential enlightenment over the training and development of this profession.
Among the speech recognition assistance tools, iFlytek Voice, Baidu Translate, and Universal Voice Translation are selected. These three versions are all Apps with a high download rate in the market, and they will be randomly used by each subject. With the help of those speech recognition assistance tools, the subjects in the test no longer need to perform note-taking, etc., saving more “cognitive efforts” for language conversion and production in the target languages.
Assessing the quality of the interpreter’s performance occurred for more than just the linguistic area, but also for the extra-linguistic area [42]. This is why we must view the interpreting performance from a broader perspective. The scoring standard of interpreting performance is based on the interpretation quality table used in the experiment by Li Shuangyan and Sun Yejiao (2021) [18], as shown in Table 1. (this scoring standard has been used many times by other scholars, and its validity and reliability are verified):
The chart above subdivides the various links of interpretation quality and assign points to each link in detail so as to assess the experiment. The quality of interpreting should be taken into consideration in every link of the process [43]. Unlike translation, interpreting places the top priority upon fidelity because the main focus is to get the message across, rather than to provide aesthetic experience, such as written a translation would do, by features such as lexical rhyme or syntactic parallelism. Therefore, the measurements in the first link will determine whether there are some misinterpreted messages, which are the major flaws in overall performance of interpreting. A total of 3% of the score will be deducted for each misinterpreted message, and 1% of the score will be deducted if there is a piece of information lost or added in the process of the test. The second link of measurement stays focused on expressiveness, cohesion, and smoothness, which constitute a better interpreting experience. As a professional in the market, providing a good customer experience is of paramount importance [23], which ranks only behind fidelity. To be specific, 2% of the score will be deducted for one expressiveness issue, and 1% of score will be deducted for one loss in cohesive and smoothness. Last but not least, the extent to which language pays attention to lexical, syntactic standards, and grammatical correctness in order to be rigorous in language usage, is considered, even though some minor mistakes in lexical, syntactic, and grammatical content may not lead to the misunderstanding of messages. Hence, 1% of the score will be deducted for one mistake at the lexical, syntactic, and grammatical level.

3.1. The First Step of the Experiment

It was necessary to judge the difference in interpreting skills between the two groups of subjects, which is the foundation for further exploration. The test is a mixed test from THE SHANGHAI ADVANCED INTERPRETATION TEST AND CATTI TEST INTERPRETER LEVEL 2, which is about to test 2 groups of subjects (GROUP 1—students interpreters; GROUP 2—professional interpreters).
Here, an independent samples t test is implemented after the first step has been completed, as shown in Table 2:
From the group statistics (descriptive statistics), the mean of the interpretation test (mean) is different between the two groups (55.13 < 74.70), and the significance of homogeneity test in the independent sample t-test is 0.409 (>0.05); therefore, the t-test of equal variances assumed is taken as: Sig. (2-tailed) = 0.000 < 0.05. Therefore, the null hypothesis is rejected, and there is a significant difference in interpreting performance between the two groups.
In order to further verify the t-test results, the independent samples non-parametric test is conducted, and the results are shown in Table 3:
The result of the independent samples non-parametric test is Sig. (2-tailed) = 0.000 < 0.05, as shown above, which proved again that the interpreting performance of professional interpreters is significantly better than that of student interpreters.

3.2. The Second Step of the Experiment

Listening comprehension is the very initial stage and one of the most difficult parts of interpreting, as mentioned by many scholars. Therefore, whether listening comprehension capacity plays a decisive role in interpreting performance should be confirmed. The correlation test between listening comprehension capacity and interpreting performance is launched.
The exercises in the listening capacity test are taken from the listening part of SHANGHAI ADVANCED INTERPRETING TEST and CATTI TEST INTERPRETERS LEVEL 2 so as to launch the test. The result is shown in Table 4:
As the correlation test indicated, the Pearson correlation was 0.681 (positive correlation), and the two-tailed test was Sig. (2-tailed) = 0.000 < 0.05. The null hypothesis was rejected, so the listening score is highly correlated with different groups; that is to say, the listening comprehension capacity of the professional group is directly positively correlated with the higher interpreting performance. Good listening comprehension capacity indicates that the professional interpreters are proficient in comprehending the input and put it in “a temporary, limited-capacity phonological store and a mechanism for sub-vocal rehearsal that keeps material in the store activated” [44]. Hence, improving the listening comprehension capacity of student interpreters will help them to bridge the gap between themselves and professional interpreters, as well as meet the needs of the job market.
Through the two tests above, it is crystal clear that listening comprehension capacity is directly related to the level of interpreting performance. The biggest shortcoming of student interpreters is their low sensitivity to second language acoustic representation, which leads to the consumption of a large amount of “cognitive energy” for listening comprehension. Therefore, language conversion and target language production require significant “cognitive energy”. For professional interpreters, listening comprehension consumes a significantly lower amount of “cognitive energy” than that consumed by the former. This leads to the following questions: Does speech recognition assistance have a positive impact upon interpreting performance for both groups? Is the increase significant?

3.3. The Third Step of the Experiment

The test of speech recognition assistance is launched, as well the determination of the difference between the interpreting performance with speech recognition assistance and the interpreting performance without speech recognition assistance, after making sure the two groups of subjects’ interpreting performance reflects a significant difference before this step of the test. In order to show how the experiment is conducted, the sample of the experiment voice recognition assistance is shown in Figure 6:
The voice recognition App demonstrated the words and sentences in front of the subjects in the test so as to help decrease their cognitive efforts. Therefore, the subjects no longer need to identify all the words of input, memorize much of the message, take notes etc.
The group of subjects without the assistance of the voice recognition App continue to take the traditional measure of note-taking, as shown in Figure 7:
Both groups of subjects are tested in the same exercise bank (a different exercise with the same level of difficulty), as mentioned previously. Instead of taking notes, both groups are assisted by the speech recognition App on the tablet. In the group of student interpreters, the result without speech recognition assistance is marked as VALUE 1, and the result with speech recognition assistance is marked as VALUE 3. The non-parametric test of two paired samples is conducted, and the result is shown in Table 5:
As clearly shown above, the mean of student interpreters is improved (71.57 > 55.13) after they are assisted by the speech recognition App. Moreover, there is no “spaced out” feeling with the speech recognition assistance when the interpreting performance of all the 30 student interpreters is improved. The results show that the interpreting performance with the speech recognition assistance (VALUE 3) is significantly higher than the interpreting performance without the speech recognition assistance (VALUE 1), Sig. (2-tailed) = 0.000 < 0.05.
The interpreting performance of professional interpreters without speech recognition assistance is labeled VALUE 2, and that with speech recognition assistance is labeled VALUE 4. Both sets are tested with a paired samples non-parametric test as well, and the result is indicated in Table 6:
The mean of professional interpreters is improved to a certain extent (80.80 > 74.70) with speech recognition assistance. However, different from the student interpreters, 14 of the 20 professional interpreters improved, 2 had the same scores, and 4 received lower scores, which indicates that the lack of adaptation of the speech recognition assistance led to the decline. The disadvantage makes itself more obvious among professional interpreters, not only will certain wrong words occur in the speech recognition App, but the “spaced-out” feeling in using an App which they have never used before will, certainly, jeopardize their interpreting performance, to a certain extent. However, the interpreting performance with the speech recognition assistance (VALUE 4) is significantly higher than the interpreting performance without the speech recognition assistance (VALUE 2), Sig. (2-tailed) = 0.002 < 0.05 on the whole.
In all results shown above, there is a significant performance increase in both groups, especially after speech recognition assistance and note-taking removal, and the information loss is significantly decreased. This is because the conversion from listening input to note symbols and decoding note symbols require significant cognitive processing, and the “cognitive energy” is significantly decreased after omitting these cognitive processing demands. Therefore, the questions remains: Is there a significant difference in the level of interpretation between student interpreters and professional interpreters when both are assisted by speech recognition?
As indicated in Table 7, after the independent sample t-test and the independent sample non-parametric test, the mean of the two groups becomes closer (71.57 < 80.80) than that without the assistance of speech recognition (55.13 < 74.70). However, there are still significant differences between the two groups of interpreters, Sig. (2-tailed) = 0.000 < 0.05. Therefore, we could see that the increase achieved by speech recognition assistance for the student interpreters is greater than that for professional interpreters. However, there is still a significant difference in interpreting performance between the two groups of interpreters, but the gap between the two has been narrowed.

3.4. The Fourth Step of the Experiment

With the aid of speech recognition, are student interpreters capable of reaching the level of professional interpreters (unassisted by speech recognition)? To answer this question, we compare the values of student interpreters (with the speech recognition assistance) with that of professional interpreters (without the speech recognition assistance). All interpretation test materials are also in the mixed test bank (different exercises with the same level of difficulty), so there is a significance of direct comparison, and the independent sample t-test and the independent sample non-parametric test are still adopted, as follows:
As shown in Table 8, no significant difference is found between the two (Sig. = 0.085 > 0.05 in t-test; non-parametric test, Sig. = 0.114 > 0.05), but according to the mean of the two groups, the level of student interpreters with the speech recognition assistance is still lower than that of professional interpreters without the speech recognition assistance (71.57 < 74.70), but the difference is not significant. In other words, with speech recognition assistance, the interpreting performance of student interpreters can reach the level of professional interpreters to a large extent, which will shed some light on the professional development of interpreters.

4. Discussion

The sustainable development of interpreters requires them to keep themselves competitive at all times, since the challenge from modern technology remains in the spotlight. Moreover, technology is capable of serving as a tool for enhancing the performance of interpreters, as well as for helping student interpreters to override their flaws, such as the listening weakness in the second language. As shown above, compared to the interpreting performance without the assistance of speech recognition, both student interpreters and professional interpreters enjoy a significant enhancement of interpreting performance with the assistance of speech recognition (Sig. = 0.000 < 0.05, Sig. = 0.002 < 0.05), which means that the use of modern technology tools should be included in interpreting work, rather than just focusing on traditional language competence enhancement. Acquiring some tools to enhance your skill may assist you, to some extent, in the job market [45,46,47] because the sustainability of professional interpreters should take advantage of all the tools on the table to fend off the challenges of being replaced by modern technology. In other words, using modern technology products to enhance interpreting performance is essential towards the professional sustainability of interpreters, and we should take more time for learning how to utilize these tools in interpreting training in order to sustain the capability to meet the ever-changing needs of the job market.
Besides meeting the demand of the market, the sustainable development of the profession in the job market lies in complementing the deficiency of student interpreters, as challenges are never absent in the real world. The majority of student interpreters cannot find themselves a secure spot in the job market because the challenge is always around the corner [23], the market never manifests any mercy toward those beginners in the interpreting profession [19]. With the help of specific technology (such as computer-assisted tools), interpreters will better tackle the issue of incoming challenges [18]. For instance, the ever-increasing flow of new terminologies and complicated expressions in listening input remain as the biggest obstacles in the initial stage of interpreting; therefore, all the work will be wasted if interpreters set foot in the wrong direction at the very beginning. The assistance of speech recognition bridges the gap between student interpreters and professional interpreters in terms of interpreting performance: the independent sample t-test and independent sample non-parametric test show the double-checked result (Sig. = 0.085 > 0.05 in the t-test; Sig. = 0.114 > 0.05 in the non-parametric test) that there is no significant gap in interpreting performance, i.e., with the help of speech recognition, student interpreters can fulfill the work which should be done by professional interpreters. As long as student interpreters are capable of reaching the professional level with the trained use of technology products, the market challenges can be better met. Beside solely improving language skills in training, the student interpreters will better engage the interpreting activities in the professional field with the use of technology assistance.
Interpreting is a type of highly “cognitive energy” consumption work, as many people have seen while viewing how those interpreters work via TV. Therefore, reducing the “cognitive burden” for interpreters should be placed as a top priority, since every procedure of interpreting takes a significant amount of “cognitive effort”. Among those procedures, listening comprehension (input) is regarded as one of the most depleting “cognitive effort”. The “burden reduction” of listening comprehension in interpreting can directly improve the efficacy of partial listening processing (such as terminology, difficult phrases, unclear acoustic information) and the overall processing (the omission of note-taking etc.). The decrease in the cognitive load of interpreters spares more “cognitive efforts” on language conversion and output [48]. However it is worth noting that speech recognition tools cannot correctly identify every word, especially in the case of accents, pronunciation preference of speakers, background music, and other interference, and there may be some errors, so the negative cognitive processing also exists in the process [49]. To be more specific, speech recognition tools may sometimes fail to catch the correct words, so as to sabotage the performance or roil the cognitive processing system. As shown in the experiment above, there were four professional interpreters who failed to enhance their interpreting performance by adopting the speech recognition assistance, since the certain possibility of wrong identification exists. However, the efficacy of speech recognition tools has been tested above: the positive effects outperform the negative, and we believe the efficacy will be reinforced to a large extent in the future with modern technology advancements (big data towards accents; computer-assisted neural voice system towards language conversion; etc.). To be honest, the flaws of technology should not be ignored, even though the efficacy will be enhanced in the future, because one mistake in a key word may ruin the whole performance; therefore, the training in the use of the technology products is of paramount importance. Specifically, the training of interpreters in using the technology products should be carried out in the university training programs in terms of the know-how of operating and identifying the wrong words due to the similar pronunciation of each word. With technology, more interpreters can identify their niches in the job market and become more competitive in dealing with an ever-changing market. There is an idea that interpreters should not “take advantage” of the speech recognition tools to improve the interpreting capability and ignore the improvement of their own language capability. In fact, the adopting of relevant modern technology does not conflict with the improvement of listening skills. For example, the use of modern instruments to improve the diagnostic effect does not undermine the improvement of the skills of doctors. The police uses modern technology to improve the efficiency of case handling, which does not hinder the capability of any police officer. Modern technology can improve the efficiency of interpretation, which should be adopted in the age of technology [50]. With the maturity and iteration of speech recognition technology, machine translation, and other technologies, interpreters are expected to use tools to improve the efficiency of pre-interpretation preparation and the accuracy of the target language in order to relieve the on-spot pressure of interpreting [51]. Therefore, acquiring the skills for utilizing the modern technology products, such as speech recognition tools, is indispensable to the sustainable development of interpreters in the job market, leading to a new direction for contemporary interpreter training.

5. The Research Outlook and Limitation

The study of technology products, such as speech recognition tools in interpreting, is conducive to the development of modern interpretation technology and further research on cognitive interpreting. Interpreting is a job with high cognitive stress, and the role of technology products in interpreting is mainly targeted at cognitive burden, This paper studies the extent of improvement of different groups of interpreters so as to explore some new ground for the theoretical and practical enhancement of interpreting in terms of technology aids. Besides interpreting, the research results also intend to serve the practical domain (development of diverse profession training and usage of technological development), since modern technologies have been “embedded” in all types of professions. Demands of the interpreting industry never stays the same, so more pioneering studies should be conducted from an industry-centered perspective [52]. Since the industry need is the primal driver of all professions, the market transformation should always be one of the focuses in academic studies in order to facilitate the sustainable development of the profession.
Specific to interpreting, speech recognition tools help improve the interpreting capability of interpreters, especially for student interpreters. While greatly reducing cognitive load and improving cognitive processing, it is possible for student interpreters to be competent in specific market interpretation work. However, more research should be conducted regarding the enhancement of interpreting performance with the assistance of various kinds of technology, inasmuch as technology now plays an indispensable role in all walks of life. Furthermore, training interpreters to better use various kinds of technology tools should to be studied, since it is the sole approach for students to learn how to take advantage of using technology tools to enhance their performance. The interpreting market should employ of versatile skills rather than just interpreting capability [53]; the ethical element of interpreting ought to be taught among interpreters, so that the secrecy or privacy of the industry can be better protected [54]. Therefore, more studies should be performed to explore other versatile skills or ethical education, etc., in future studies to better fit the industry. This study has made a dual comparison (student interpreters and professional interpreters, along with the subjects with the speech recognition assistance and the subjects without the speech recognition assistance) to explore the professional sustainability of both student interpreters and professional interpreters. However, the study takes the listening comprehension (input of the interpreting process) as the cognitive target, but there is more work to be done, such as investigating the cognitive process of language conversion, output, etc., in the future to further explore the professional sustainability among interpreters.

Author Contributions

Writing—original draft, Y.H.; Writing—review & editing, W.S. and J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Traditional consecutive interpreting.
Figure 1. Traditional consecutive interpreting.
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Figure 2. Consecutive interpreting with speech recognition assistance.
Figure 2. Consecutive interpreting with speech recognition assistance.
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Figure 3. Qualification of interpreters.
Figure 3. Qualification of interpreters.
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Figure 4. Language competence of interpreters.
Figure 4. Language competence of interpreters.
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Figure 5. Tools for interpreters.
Figure 5. Tools for interpreters.
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Figure 6. Sample of speech recognition App.
Figure 6. Sample of speech recognition App.
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Figure 7. Note-taking.
Figure 7. Note-taking.
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Table
Table 1. The interpretation quality table.
Table 1. The interpretation quality table.
ItemsRatio
1. Fidelity
A. Misinterpreting30%
B. Loss of Information10%
C. Redundant Information10%
2. Delivery
A. Expressiveness 20%
B. Cohesion and Smoothness10%
3. Language
A. Lexical and Syntactic 15%
B. Grammatical Correctness5%
Table
Table 2. The independent samples t test.
Table 2. The independent samples t test.
Group Statistics
GROUPNMeanStd. DeviationStd. Error Mean
VALUE13055.137.9081.444
22074.707.1901.608
Independent Samples Test
Levene’s Test for Equality of Variancest-test for Equality of Means
FSig.tdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference
LowerUpper
VALUEEqual variances assumed0.6940.409−8.881480.000−19.5672.203−23.996−15.137
Equal variances not assumed −9.05543.4770.000−19.5672.161−23.923−15.210
Table
Table 3. The independent samples non-parametric test.
Table 3. The independent samples non-parametric test.
Test Statistics a
VALUE
Mann–Whitney U14.000
Wilcoxon W479.000
Z−5.667
Asymp. Sig. (2-tailed)0.000
a Grouping variable: GROUP.
Table
Table 4. The Pearson correlation test.
Table 4. The Pearson correlation test.
Correlations
GROUPVALUE
GROUPPearson Correlation10.681 **
Sig. (2-tailed) 0.000
N5050
VALUEPearson Correlation0.681 **1
Sig. (2-tailed)0.000
N5050
** Correlation is significant at the 0.01 level (2-tailed).
Table
Table 5. The non-parametric test of two paired samples.
Table 5. The non-parametric test of two paired samples.
Descriptive Statistics
NMeanStd. DeviationMinimumMaximumPercentiles
25th50th (Median)75th
VALUE13055.137.908437048.7555.0061.25
VALUE33071.575.412638768.7569.5074.25
Ranks
NMean RankSum of Ranks
VALUE3—VALUE1Negative Ranks0 a0.000.00
Positive Ranks30 b15.50465.00
Ties0 c
Total30
a VALUE3 < VALUE1
b VALUE3 > VALUE1
c VALUE3 = VALUE1
Test Statistics a
VALUE3—VALUE1
Z−4.786 b
Asymp. Sig. (2-tailed)0.000
a Wilcoxon signed ranks test; b Based on negative ranks.
Table
Table 6. The paired samples non-parametric test.
Table 6. The paired samples non-parametric test.
Descriptive Statistics
NMeanStd. DeviationMinimumMaximumPercentiles
25th50th (Median)75th
VALUE22074.707.190628969.0073.0080.00
VALUE42080.805.827688976.5079.5086.50
Ranks
NMean RankSum of Ranks
VALUE4—VALUE2Negative Ranks4 a3.7515.00
Positive Ranks14 b11.14156.00
Ties2 c
Total20
a VALUE4 < VALUE2
b VALUE4 > VALUE2
c VALUE4 = VALUE2
Test Statistics a
VALUE4—VALUE2
Z−3.074 b
Asymp. Sig. (2-tailed)0.002
a Wilcoxon signed ranks test. b Based on negative ranks.
Table
Table 7. The independent sample t-test and the independent sample non-parametric test.
Table 7. The independent sample t-test and the independent sample non-parametric test.
Group Statistics
GROUPNMeanStd. DeviationStd. Error Mean
VALUE13071.575.4120.988
22080.805.8271.303
Independent Samples Test
Levene’s Test for Equality of Variancest-test for Equality of Means
FSig.tdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference
LowerUpper
VALUEEqual variances assumed0.4450.508−5.732480.000−9.2331.611−12.472−5.995
Equal variances not assumed −5.64638.7400.000−9.2331.635−12.542−5.925
Test Statistics a
VALUE
Mann–Whitney U77.500
Wilcoxon W542.500
Z−4.420
Asymp. Sig. (2-tailed)0.000
a Grouping variable: GROUP.
Table
Table 8. The independent sample t-test and The independent sample non-parametric test.
Table 8. The independent sample t-test and The independent sample non-parametric test.
Group Statistics
GROUPNMeanStd. DeviationStd. Error Mean
VALUE13071.575.4120.988
22074.707.1901.608
Independent Samples Test
Levene’s Test for Equality of Variancest-test for Equality of Means
FSig.tdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference
LowerUpper
VALUEEqual variances assumed2.1310.151−1.757480.085−3.1331.783−6.7190.452
Equal variances not assumed −1.66032.9810.106−3.1331.887−6.9730.706
Test Statistics a
VALUE
Mann-Whitney U220.500
Wilcoxon W685.500
Z−1.583
Asymp. Sig. (2-tailed)0.114
a Grouping variable: GROUP.
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Huang, Y.; Shi, W.; Wen, J. Technology Challenges and Aids: The Sustainable Development of Professional Interpreters in Listening Comprehension Effectiveness and Interpreting Performance. Sustainability 2023, 15, 6828. https://doi.org/10.3390/su15086828

AMA Style

Huang Y, Shi W, Wen J. Technology Challenges and Aids: The Sustainable Development of Professional Interpreters in Listening Comprehension Effectiveness and Interpreting Performance. Sustainability. 2023; 15(8):6828. https://doi.org/10.3390/su15086828

Chicago/Turabian Style

Huang, Yuwei, Weinan Shi, and Jinglin Wen. 2023. "Technology Challenges and Aids: The Sustainable Development of Professional Interpreters in Listening Comprehension Effectiveness and Interpreting Performance" Sustainability 15, no. 8: 6828. https://doi.org/10.3390/su15086828

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