بهبود تشخیص گفتار چند شخصی
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بهبود تشخیص گفتار چند شخصی

عنوان فارسی مقاله: بهبود تشخیص گفتار چند شخصی تک کاناله انتها به انتها
عنوان انگلیسی مقاله: Improving End-to-End Single-Channel Multi-Talker Speech Recognition
مجله/کنفرانس: تعاملات IEEE/ACM درباره صدا، گفتار و پردازش زبان – IEEE/ACM Transactions on Audio, Speech, and Language Processing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: هوش مصنوعی
کلمات کلیدی فارسی: تشخیص گفتار ترکیبی چند شخصی، تبدیل تغییر ناپذیر آموزش، مدل انتها به انتها، چکیده دانش، یادگیری برنامه ای
کلمات کلیدی انگلیسی: Multi-talker mixed speech recognition, permutation invariant training, end-to-end model, knowledge distillation, curriculum learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1109/TASLP.2020.2988423
دانشگاه: Shanghai Jiao Tong University, Shanghai, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: کنفرانس
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 3.398 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E15095
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- INTRODUCTION


2- SINGLE-CHANNEL MULTI-TALKER SPEECH RECOGNITION


3- END-TO-END MULTI-TALKER SPEECH RECOGNITION


4- EXPERIMENT


5- CONCLUSION


REFERENCES

نمونه متن انگلیسی مقاله

Abstract


Although significant progress has been made in singletalker automatic speech recognition (ASR), there is still a large performance gap between multi-talker and single-talker speech recognition systems. In this article, we propose an enhanced end-toend monaural multi-talker ASR architecture and training strategy to recognize the overlapped speech. The single-talker end-to-end model is extended to a multi-talker architecture with permutation invariant training (PIT). Several methods are designed to enhance the system performance, including speaker parallel attention, scheduled sampling, curriculum learning and knowledge distillation.More specifically, the speaker parallel attention extends the basic single shared attention module into multiple attention modules for each speaker, which can enhance the tracing and separation ability. Then the scheduled sampling and curriculum learning are proposed to make the model better optimized. Finally the knowledge distillation transfers the knowledge from an original single-speaker model to the current multi-speaker model in the proposed end-to-end multi-talker ASR structure. Our proposed architectures are evaluated and compared on the artificially mixed speech datasets generated from the WSJ0 reading corpus. The experiments demonstrate that our proposed architectures can significantly improve the multi-talker mixed speech recognition. The final system obtains more than 15% relative performance gains in both character error rate (CER) and word error rate (WER) compared to the basic end-to-end multi-talker ASR system.


INTRODUCTION


T HANKS to the advances in deep learning, automatic speech recognition (ASR) has achieved a huge progress. Deep neural networks (DNN) and hiddenMarkov model (HMM) based hybrid systems have achieved a very good performance, which are comparable with, or even surpassing, human performance [1]–[۳]. Recently, there have been growing interests in developing end-to-end systems for speech recognition, in which multiple modules in the hybrid systems, such as the acoustic model (AM), lexicon model, and language model (LM), are folded into a single neural network model, so that they can be optimized simultaneously. Over the past few years, a variety of end-to-end (E2E) models have been proposed and they can be mainly categorized into connectionist temporal classification (CTC) based models [4], [5], and sequence to sequence (S2S) based models [6], [7]. The combined mode with both CTC and S2S [8] is also designed to further improve the end-to-end ASR system. The end-to-end systems have shown promising results according to existing works [8]–[۱۰]. On the other hand, although a huge progress has been achieved on ASR, the current systems mainly focus on single-talker speech, and there is still a large performance gap between single-talker and multi-talker speech recognition. Processing the multi-talker mixed speech is a key problem when multi-talker mixed speech commonly exists in the complex real-world conditions, especially under the cocktail party scenarios [11]–[۱۳].

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