Conference proceeding
An investigation of phone-based subword units for end-to-end speech recognition
INTERSPEECH 2020, Vol.2020-, pp.1778-1782
Interspeech
01/01/2020
DOI: 10.21437/Interspeech.2020-1873
Abstract
Phones and their context-dependent variants have been the standard modeling units for conventional speech recognition systems, while characters and subwords have demonstrated their effectiveness for end-to-end recognition systems. We investigate the use of phone-based subwords, in particular, byte pair encoder (BPE), as modeling units for end-to-end speech recognition. In addition, we also developed multi-level language model-based decoding algorithms based on a pronunciation dictionary. Besides the use of the lexicon, which is easily available, our system avoids the need of additional expert knowledge or processing steps from conventional systems. Experimental results show that phone-based BPEs tend to yield more accurate recognition systems than the character-based counterpart. In addition, further improvement can be obtained with a novel one-pass joint beam search decoder, which efficiently combines phone- and character-based BPE systems. For Switchboard, our phone-based BPE system achieves 6.8%/14.4% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6.3%/13.3% WER. On Fisher + Switchboard, joint decoding leads to 4.9%/9.5% WER, setting new milestones for telephony speech recognition.
Details
- Title: Subtitle
- An investigation of phone-based subword units for end-to-end speech recognition
- Creators
- Weiran Wang - SalesforceGuangsen Wang - SalesforceAadyot Bhatnagar - SalesforceYingbo Zhou - SalesforceCaiming Xiong - SalesforceRichard Socher - Salesforce
- Resource Type
- Conference proceeding
- Publication Details
- INTERSPEECH 2020, Vol.2020-, pp.1778-1782
- Publisher
- Isca-Int Speech Communication Assoc
- Series
- Interspeech
- DOI
- 10.21437/Interspeech.2020-1873
- ISSN
- 2308-457X
- eISSN
- 1990-9772
- Number of pages
- 5
- Language
- English
- Date published
- 01/01/2020
- Academic Unit
- Computer Science
- Record Identifier
- 9984696568502771
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