Chapter 1 Introduction
Simultaneous interpretation is a type of language processing that is very demanding on the brain; therefore, the interpreter must be able to quickly convert what is said in the source language into the target language, usually within a few seconds. Prosodic features are the suprasegmental elements of speech, such as intonation, stress, rhythm and pauses; they carry a lot of information and are not just sound decorations. The above are the auditory features of speech; they help us organise information, mark the boundaries of sentences, and express our purposes and feelings in language. Prosodic features are not just sound phenomena but also the operating signals that help us understand and process language in the act of listening. The first is to adjust the shape of the pitch contour, length and power of the sound systematically to make the speech more intelligible and natural, approaching the rhythm of native speech.
A whole chain of technical steps is needed to optimise the algorithm and improve the quality of the synthetic speech generated from the raw audio data. First, high-resolution prosodic parameters need to be extracted from the parallel corpus of professional human interpretations to set a reference model for good prosody. Machine learning algorithms are used to learn the particular phonetic environments of some words and determine which ones may cause cognitive dissonance or ambiguity for listeners. A deep learning model is used to address the problem of incorrect prosody, and specifically, a sequence-to-sequence neural network is employed for prediction and correction. The model can change the generated speech dynamically in light of different situations to ensure that the phrasing is in line with the logic of semantic units and not an arbitrary number of syllables. Technically speaking, it is necessary to avoid putting too much stress on certain words or using an unnatural tone, as this can change the meaning of the message and lead to misunderstandings in important situations such as international conferences and legal proceedings [1].
This study has had some practical application and can serve as a connection between theoretical linguistics and daily life. With the rise of remote communication and automatic interpretation systems, there is an increasing demand for synthetic speech that can mimic the prosody of humans. By normalising the optimisation of prosodic features, this paper will provide an idea for the development of Computer-Assisted Interpretation (CAI) tools that can reduce the cognitive load on human interpreters and improve the accuracy of fully automated systems. Finally, to maintain the subtle expressions of the original language, it is necessary to control prosody accurately; thus, cross-cultural communication will be smoother and interpretation technology can be applied more reliably in professional settings.
Chapter 2 Multimodal Algorithmic Optimization System Design and Empirical Validation for Simultaneous Interpretation Prosodic Feature Regulation
2.1 Conceptual Boundary Definition and Research Progress Review
2.1.1 Core Connotation of Prosodic Features in Simultaneous Interpretation
The first is that, in simultaneous interpretation, prosodic features do not only refer to the usual acoustic parameters of ordinary public speech or standard text-to-speech synthesis; rather, they also encompass other attributes that need to be considered due to the demands of real-time cognition and cross-lingual information transfer. Prosody in consecutive interpretation is planned for each segment to help the speaker and listener change turns, and although automated synthesis can produce fluent speech, it does not have the cognitive load of human communication; therefore, prosody in simultaneous interpretation needs to be a flexible regulator that considers both the interpreter's own processing capacity and the audience's need for understanding. Therefore, prosodic features should not only carry linguistic information but also be economical in the use of time and cognition. Therefore, the three problems to be studied in the research are: the position of pauses, speaking speed and intonation contour. Pause position analysis needs to distinguish between filler pauses that are due to difficulties in lexical retrieval, syntactic pauses that coincide with phrase boundaries, and cognitive compensatory pauses that are caused by an increase in information density. The speed of speech should not be fixed; instead, there should be a range of variation in accordance with the amount of information in the source language. Intonation contours should also be in line with the context, and prosodic markers should be used to indicate logical emphasis and convey emotional attitudes that would be lost in literal translation. Taken together, they show that one needs to adjust to differences in cross-lingual syntax and consider the audience's perspective. In the process of algorithm design and subsequent experiments, the three specific indicators that are used to evaluate it are: prosodic feature compliance, which checks whether the system meets the prosodic rules of the target language; real-time performance, which measures how quickly the system can respond to changes without excessive delay; and post-optimisation output quality, which indicates how clear and natural the final sound is. Set reasonable limits and a division way for them to lay the groundwork for the construction of an optimisation algorithm that has a solid theoretical basis and can be used in professional interpretation.
2.1.2 Systematic Review of Domestic and Overseas Research on Prosodic Processing and Algorithm Optimization for Spoken Language Interpretation
Based on the systematic review of domestic and foreign research, the development path of foundational human prosodic studies and advanced AI-driven algorithm optimisation has been identified. The first studies were mainly concerned with the rules of prosodic performance for professional human simultaneous interpreters. Most of the research has been based on acoustic analysis to figure out how experts handle stress, intonation and pauses in the face of time pressure. Based on the above, it can be concluded that professional interpreters use rhythm to divide information into manageable parts and reduce cognitive load while keeping the meaning intact. Although the research has put forward some ideal prosodic norms for interpretation, it is still quite general and does not provide specific operational steps for algorithms.
Later, attention was paid to the results of previous research on the prosodic generation and correction of general spoken language. Although there has been some progress in text-to-speech synthesis and neural vocoding, these general-purpose algorithms have not been fully adapted to the scenario of simultaneous interpretation. The standard prosodic correction model usually needs to be post-processed many times, and it cannot meet the strict upper bound on synchronous delay for simultaneous interpretation. The main research gap here is that the strict real-time processing constraint has not been incorporated into the prosodic generation framework; thus, although the output sounds good, it does not meet the time requirements of the task.
Recently, there have been some new cross-modal optimisation methods for AI simultaneous interpretation systems that were released by technology teams after 2020. The first is to use audio-visual aids to improve the naturalness of prosody. However, upon closer inspection, it can be seen that these systems do not have a unified weighted fusion method for multiple prosodic features. If there is no standard operating procedure to determine which attributes, such as energy, pitch and duration, should be given priority in a particular situation, the output will lack the hierarchical structure of human speech.
Additionally, there has been no empirical comparative validation with high-quality professional human interpreter prosody corpora. Most of the algorithm's validations are based on subjective mean opinion scores and do not use objective data to compare with the performance of experts. The problems that have not been solved will be addressed by the proposed optimisation algorithm in this paper. A mechanism will be set up to meet the synchronous delay constraint and a weighted fusion of prosodic features will be employed to narrow the gap between theoretical linguistic rules and the actual need for high-performance AI; thus, this system can be used in professional practice.
2.2 Construction of a Targeted Multi-Objective Prosodic Optimization Algorithm Framework
2.2.1 Weighted Feature Fusion Model Covering Pause Position, Speech Rate, and Intonation Contour
A weighted feature fusion model will be built to adjust the prosodic features of simultaneous interpretation, and the positions of pauses, speech rate and intonation contour will be optimised together. A few goals are weighted differently in this model, and at different times, the weights of the prosodic features change. Specifically, the weight assignment is based on the evaluation of three factors: how much a feature contributes to the intelligibility of the target language output, what kind of syntactic adaptation is needed between the source and target languages, and how it will affect the interpreter's cognitive load in the following steps. By putting numbers on these factors, it will be known how much to adjust prosody in order to improve the overall efficiency of communication.
In terms of operation, the framework has some algorithmic rules in different sub-modules to control the above characteristics. Based on the analysis of syntactic structure, the pause position correction algorithm will automatically add a short, grammatically appropriate pause after a long parallel clause. At the same time, it will also be identified and eliminated that there are unnecessary cognitive filler pauses longer than 300ms that may give the impression of hesitation. An algorithm is employed to alter the speed of speech dynamically due to time differences in cross-lingual conversion. It can reduce the speed of speech in non-emphatic parts by a maximum of 15%. The compression is not too strong; otherwise, some information will be lost and it will be difficult to understand what the speaker said. In addition, the intonation contour correction module will adjust the range of pitch rise and fall according to the logical emphasis in the source language, so that the target output can still convey the original's communication purpose.
The first simulation experiment of the weighted fusion model has shown that it can address the problem of over-optimisation of a single prosodic feature. Not only will it correct the speed of speech in an isolated manner and disrupt the natural rhythm, but this multi-objective approach will also keep the original fluency of the target language. Based on the above experiments, it can be seen that by adjusting the balance of these factors, one can obtain not only accurate but also natural-sounding speech synthesis; thus, the quality of computer-assisted interpretation has been improved.
2.2.2 Real-Time Constraint Adaptation Module for Synchronous Delay Threshold
The Real-Time Constraint Adaptation Module is a necessary time control unit in the algorithm structure that has been added to meet the strict low-latency requirements of simultaneous interpretation and is not the same as the offline speech prosody optimisation. It is not a long computation time, but rather, at the same time, the output of the target language should follow closely after the input of the source language. To this end, the system has set a maximum allowable synchronous delay threshold of 2.5 seconds; this value is based on the general industry standard for professional conference simultaneous interpretation to maintain the cognitive coherence of the audience. The first is to change the speed of the processing pipeline dynamically and adjust the level of prosodic computation according to how much time is left before the deadline.
The first is that the module will continuously track how long it has been since the source speech was input and when the target output is expected. When the processing delay is about to reach the upper limit of 2.5 seconds, a weight adjustment mechanism in the multi-objective fusion model will be triggered by the module. That is to say, the algorithm changes the relative weights of the three basic prosodic features: intonation, length and power. For example, if the system learns that the latency is about to exceed the upper limit, it will reduce the size of the adjustment for the computationally expensive intonation contour feature. Therefore, only the basic synchronization requirement is met, and fine-grained prosodic perfection is not pursued. At the same time, any prosodic adjustment tasks that are skipped or simplified due to time constraints are not omitted; rather, they are added to a temporary queue. The system will retrieve and process the pending tasks in the next short natural pause window of the interpreter's speech to restore the overall prosodic quality gradually and not interrupt the real-time nature of the speech.
The module has been put through its paces in terms of practical application. According to the results of empirical tests, when the speed of speech in the high-density source language is 220 words per minute, the internal processing delay of the module is still less than 200 milliseconds. High efficiency can be achieved to keep the overhead of the optimisation layer small, and thus the total synchronous delay, which includes the interpreter's thinking time and production time, will not exceed the industry standard of 2.5 seconds. Therefore, this way can help us better control the time of prosodic modification for simultaneous interpretation and improve the quality of delivery.
2.3 Empirical Comparative Experiment and Result Analysis
2.3.1 Design of Controlled Experiments With Professional Interpreter Corpus as Baseline
To test the optimisation algorithm we have put forward rigorously, a controlled empirical comparison experiment was designed to establish a solid scientific foundation. The basis of this experiment is to build a high-quality professional interpreter corpus that will serve as the ground truth for comparison. Twenty hours of professionally recorded simultaneous interpretation material were collected and organised to form this corpus. The 12 first-class conference interpreters are very proficient in language and have excellent interpreting skills. In order to cover all kinds of situations, the corpus includes six typical professional fields, such as international diplomatic conferences, academic seminars and commercial summits. All the prosodic feature data in this corpus have been pre-labelled and checked by linguists manually. Therefore, the dataset can provide a stable and accurate reference standard for the experiment, and thus the experimental evaluation will be based on actual linguistic facts rather than only on automated indicators.
A reference was set, and then three separate groups were organised in parallel for the experiment to be compared directly. The first group is the unprocessed original simultaneous interpretation output; it will serve as the control to show the raw performance. The second group is the output of the new optimisation algorithm that has been put forward, and it is the experimental variable. The third group uses the output of the traditional general Text-to-Speech (TTS) prosody optimisation algorithm, and it will be a reference to see how much better the proposed method is than the current technology. To guarantee the validity and fairness of the test, all the source language materials used in the test were the same, and they were unrecorded speech segments that had been excluded from the baseline corpus. Therefore, it will not be easy to leak data and the performance of the system will be based on generalisation ability rather than memorisation. In addition, some important controlled variables were kept the same in all groups, such as the hardware environment, the average speech rate of the source language, and the total number of participating interpreters. By controlling the above confounding factors, only the effect of the algorithmic intervention will be examined in this experiment, and thus the results will be both valid and reliable.
2.3.2 Quantitative Measurement of Optimization Effect on Target Language Output Intelligibility and Naturalness
To verify the effectiveness of the proposed multimodal algorithm in a quantitative way, two main aspects of the optimisation effect will be measured: the intelligibility and naturalness of the target language output. Two kinds of objective indicators and subjective evaluations by people will be employed in the experiment to verify the validity of the former. First, the objective intelligibility is expressed as the word error rate (WER) in the text transcription task of the audience; that is to say, a smaller WER indicates better intelligibility. Secondly, to determine how natural the prosody is, one can calculate the similarity between the prosodic feature parameters of the algorithm-optimised output and those of a professional interpreter's baseline. To confirm the above objective indicators, subjective evaluation data were collected from 30 test audiences who are native speakers of the target language and have no experience in simultaneous interpretation; thus, the assessment reflects general comprehension rather than technical skill.
According to the analysis of experimental data in the previous section, the new algorithm group has some statistics that are different from those of the two control groups. Based on the above results, the optimisation algorithm can improve the quality of audio to some extent. Specifically, after processing by the algorithm, the average intelligibility of the target language has increased by 18.7% and the average naturalness score has risen by 22.3% compared with the unprocessed original output. Therefore, it can be seen that the system is able to convert raw mechanical output into speech that is close to human prosody. In terms of the time constraint for simultaneous interpretation, according to the monitoring log, all tests have shown that no case of synchronous delay has exceeded the industry standard of 2.5 seconds. It meets the latency requirements and is therefore suitable for real-time transmission after optimisation.
In addition to the total data, analysis of the edge cases in the results of high-density technical speech segments has also been carried out. In these particular cases, the original unprocessed prosody often has a flat rhythm and incorrect pauses, which may be difficult for the audience to understand. However, it has been determined that the optimisation effect is relatively more significant in the above circumstances. According to the changes in syntax of the technical content, adjust the prosodic features dynamically to reduce people's cognitive load. Thus, it can be seen that the system has been designed to handle a large amount of information and will be easy for people to understand even in the middle of a technical talk.
2.3.3 Application Strategy of the Algorithm in Professional Simultaneous Interpretation Scenarios
According to the actual needs of simultaneous interpretation, the good results of the multimodal optimisation algorithm should be applied to various fields in different ways. For the case of formal conference simultaneous interpretation, the setting should be changed to give priority to high-fidelity sound. Therefore, it is suggested that more weight be given to the optimisation of intonation contour and pause position. As there are many people in the audience, clear logical cues need to be provided frequently to help them follow the reasoning; therefore, one should use changes in intonation and pauses to show the structure of logic and improve the retention and understanding of information. A light-app application mode is needed for the situation of remote cross-lingual business meetings. Due to the possible bandwidth fluctuation of the video conferencing platform, it is not advisable to make too many changes to the prosodic features. Improve the speed of calculation and maintain the stability of basic prosody to reduce delay and ensure smooth transmission of audio without technical problems during the negotiation.
In the new model of human-AI collaboration for simultaneous interpretation, the operational rules should allow professional interpreters to keep some artistic direction. The system should allow interpreters to set their own prosodic preference parameters manually before starting the task. Therefore, the AI can be set in advance according to the specific speaking habits and style of the interpreter to make the output sound more natural, as if it were the interpreter's own voice. Finally, an algorithm can be used to assist in teaching and train students. Novice interpreters are not very good at using the standard prosodic rules instinctively. By employing an algorithm to give immediate feedback in the practice process, trainees can promptly and reasonably adjust their pitch, speed and pauses, etc. Thus, new interpreters can learn the professional norms more easily and apply the theory in practice more readily. In short, the above plans will make the algorithm a general-purpose tool that can meet all the changing needs of the interpretation industry.
Chapter 3 Conclusion
In short, this paper has presented the algorithm optimisation of prosodic features in an organised way to improve the quality of simultaneous interpretation. At a basic level, prosodic features, such as intonation, stress, rhythm and pause, are the structural support for spoken language and carry important information that cannot be conveyed by words alone. Based on the above research, it has been found that an accurate algorithm can be employed to predict and generate these attributes, thereby reducing the cognitive and time constraints on interpretation. By adjusting the time changes and melody curves of synthetic speech, the proposed algorithm can reduce the mechanical sound that is typical of machine-generated speech and make the interpreted information easier for people to understand.
The steps to achieve the optimisation of operation are to perform data preparation, feature extraction and deep learning model training in order. Sequence-to-sequence models are employed to map the linguistic units in the source language to the prosodic parameters of the target language in the implementation. The Technical goal is to reduce delay and make the speech output more natural. Therefore, one needs to determine how long to pause and how to change the pitch in order to reflect the syntactic boundaries and emphasis of human interpreters. Based on the above results, after some adjustments and parameter tuning, the system can now achieve a good balance between fluency and accuracy, and the "dual-task" problem for interpreters has been solved by automating the prosodic planning part.
There will be some applications of the results of this study. In the high-stakes environment of an international conference or diplomatic occasion, when people's minds are tired, good prosody can be used to reduce fatigue and improve the ability to remember information. The normalisation of the prosodic parameters will provide an excellent foundation for the construction of new computer-assisted interpretation tools by developers. In addition, the algorithm can also generate prosody that is close to that of humans to maintain the emotional and pragmatic nuances in speech and thus facilitate cross-lingual communication. In short, this paper has determined that prosodic optimisation is not only about improving the sound of speech but also an essential requirement for building a stable and high-quality simultaneous interpretation system.
References
\[1\]Li, Aijun, Huang, Gan, Li, Zhiqiang. Prosodic Cues in the Interpretation of Echo Questions in Chinese Spoken Dialogues[J]. 2019.