Active Learning Methods for Low Resource End-to-End Speech Recognition

2019 ◽  
Author(s):  
Karan Malhotra ◽  
Shubham Bansal ◽  
Sriram Ganapathy
Keyword(s):  
Speech Recognition ◽  
Active Learning ◽  
Learning Methods ◽  
Low Resource ◽  
End To End

scholarly journals Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3063
Author(s):  
Aleksandr Laptev ◽  
Andrei Andrusenko ◽  
Ivan Podluzhny ◽  
Anton Mitrofanov ◽  
Ivan Medennikov ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Error Rate ◽  
Rapid Development ◽  
Computational Cost ◽  
Vocabulary Size ◽  
Word Error Rate ◽  
Low Resource ◽  
Steady Improvement ◽  
End To End ◽  
Asr System

With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. For on-device speech recognition tasks, researchers and industry prefer end-to-end ASR systems as they can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Personalization, which is mainly handling out-of-vocabulary (OOV) words, is another challenging task associated with speech assistants. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. We propose a method of dynamic acoustic unit augmentation based on the Byte Pair Encoding with dropout (BPE-dropout) technique. The method non-deterministically tokenizes utterances to extend the token’s contexts and to regularize their distribution for the model’s recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative word error rate (WER) and 25% relative F-score) at no additional computational cost. Owing to the BPE-dropout use, our monolingual Turkish Conformer has achieved a competitive result with 22.2% character error rate (CER) and 38.9% WER, which is close to the best published multilingual system.

scholarly journals Cross-Language End-to-End Speech Recognition Research Based on Transfer Learning for the Low-Resource Tujia Language

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 179 ◽  
Author(s):  
Chongchong Yu ◽  
Yunbing Chen ◽  
Yueqiao Li ◽  
Meng Kang ◽  
Shixuan Xu ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Recognition System ◽  
Language Recognition ◽  
Low Resource ◽  
End To End ◽  
The Cross ◽  
Hidden Layer ◽  
Cross Language

To rescue and preserve an endangered language, this paper studied an end-to-end speech recognition model based on sample transfer learning for the low-resource Tujia language. From the perspective of the Tujia language international phonetic alphabet (IPA) label layer, using Chinese corpus as an extension of the Tujia language can effectively solve the problem of an insufficient corpus in the Tujia language, constructing a cross-language corpus and an IPA dictionary that is unified between the Chinese and Tujia languages. The convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) network were used to extract the cross-language acoustic features and train shared hidden layer weights for the Tujia language and Chinese phonetic corpus. In addition, the automatic speech recognition function of the Tujia language was realized using the end-to-end method that consists of symmetric encoding and decoding. Furthermore, transfer learning was used to establish the model of the cross-language end-to-end Tujia language recognition system. The experimental results showed that the recognition error rate of the proposed model is 46.19%, which is 2.11% lower than the that of the model that only used the Tujia language data for training. Therefore, this approach is feasible and effective.

End-to-End Articulatory Attribute Modeling for Low-Resource Multilingual Speech Recognition

2019 ◽  
Author(s):  
Sheng Li ◽  
Chenchen Ding ◽  
Xugang Lu ◽  
Peng Shen ◽  
Tatsuya Kawahara ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Low Resource ◽  
Multilingual Speech Recognition ◽  
End To End

scholarly journals Loss Prediction: End-to-End Active Learning Approach For Speech Recognition

2021 ◽  
Author(s):  
Jian Luo ◽  
Jianzong Wang ◽  
Ning Cheng ◽  
Jing Xiao
Keyword(s):  
Speech Recognition ◽  
Active Learning ◽  
Learning Approach ◽  
Loss Prediction ◽  
End To End

scholarly journals Exploring End-to-End Techniques for Low-Resource Speech Recognition

2018 ◽  
pp. 32-41 ◽  
Author(s):  
Vladimir Bataev ◽  
Maxim Korenevsky ◽  
Ivan Medennikov ◽  
Alexander Zatvornitskiy
Keyword(s):  
Speech Recognition ◽  
Low Resource ◽  
End To End

scholarly journals Improving low-resource Tibetan end-to-end ASR by multilingual and multilevel unit modeling

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Siqing Qin ◽  
Longbiao Wang ◽  
Sheng Li ◽  
Jianwu Dang ◽  
Lixin Pan
Keyword(s):  
Speech Recognition ◽  
Transfer Learning ◽  
Recognition Performance ◽  
Low Resource ◽  
Learning Framework ◽  
Positive Effects ◽  
End To End ◽  
Historical Heritage ◽  
First Time ◽  
Asr System

AbstractConventional automatic speech recognition (ASR) and emerging end-to-end (E2E) speech recognition have achieved promising results after being provided with sufficient resources. However, for low-resource language, the current ASR is still challenging. The Lhasa dialect is the most widespread Tibetan dialect and has a wealth of speakers and transcriptions. Hence, it is meaningful to apply the ASR technique to the Lhasa dialect for historical heritage protection and cultural exchange. Previous work on Tibetan speech recognition focused on selecting phone-level acoustic modeling units and incorporating tonal information but underestimated the influence of limited data. The purpose of this paper is to improve the speech recognition performance of the low-resource Lhasa dialect by adopting multilingual speech recognition technology on the E2E structure based on the transfer learning framework. Using transfer learning, we first establish a monolingual E2E ASR system for the Lhasa dialect with different source languages to initialize the ASR model to compare the positive effects of source languages on the Tibetan ASR model. We further propose a multilingual E2E ASR system by utilizing initialization strategies with different source languages and multilevel units, which is proposed for the first time. Our experiments show that the performance of the proposed method-based ASR system exceeds that of the E2E baseline ASR system. Our proposed method effectively models the low-resource Lhasa dialect and achieves a relative 14.2% performance improvement in character error rate (CER) compared to DNN-HMM systems. Moreover, from the best monolingual E2E model to the best multilingual E2E model of the Lhasa dialect, the system’s performance increased by 8.4% in CER.

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