Title
Improving RNN Transducer Based ASR with Auxiliary Tasks
Abstract
End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers. Specifically, recurrent neural network transducer (RNN-T) has shown competitive ASR performance on various benchmarks. In this work, we examine ways in which RNN-T can achieve better ASR accuracy via performing auxiliary tasks. We propose (i) using the same auxiliary task as primary RNN-T ASR task, and (ii) performing context-dependent graphemic state prediction as in conventional hybrid modeling. In transcribing social media videos with varying training data size, we first evaluate the streaming ASR performance on three languages: Romanian, Turkish and German. We find that both proposed methods provide consistent improvements. Next, we observe that both auxiliary tasks demonstrate efficacy in learning deep transformer encoders for RNN-T criterion, thus achieving competitive results -2.0%/4.2% WER on LibriSpeech test-clean/other - as compared to prior top performing models.
Year
DOI
Venue
2021
10.1109/SLT48900.2021.9383548
2021 IEEE Spoken Language Technology Workshop (SLT)
Keywords
DocType
ISSN
recurrent neural network transducer,speech recognition,auxiliary learning
Conference
2639-5479
ISBN
Citations 
PageRank 
978-1-7281-7067-1
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Chunxi Liu184.20
Frank Zhang2106.00
Duc-Trong Le3156.08
Suyoun Kim4356.15
Yatharth Saraf531.07
Geoffrey Zweig63406320.25