End-to-end keyword search system based on attention mechanism and energy scorer for low resource languages

2021 ◽  
Author(s):  
Zeyu Zhao ◽  
Wei-Qiang Zhang
Keyword(s):  
Keyword Search ◽  
Attention Mechanism ◽  
Search System ◽  
Low Resource ◽  
End To End

The 2016 RWTH Keyword Search System for Low-Resource Languages

2017 ◽  
pp. 719-730 ◽  
Author(s):  
Pavel Golik ◽  
Zoltán Tüske ◽  
Kazuki Irie ◽  
Eugen Beck ◽  
Ralf Schlüter ◽  
...  
Keyword(s):  
Keyword Search ◽  
Search System ◽  
Low Resource

End-to-end speech recognition and keyword search on low-resource languages

2017 ◽  
Author(s):  
Andrew Rosenberg ◽  
Kartik Audhkhasi ◽  
Abhinav Sethy ◽  
Bhuvana Ramabhadran ◽  
Michael Picheny
Keyword(s):  
Speech Recognition ◽  
Keyword Search ◽  
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.

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