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        <title>Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce</title>
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    <abstract>Automatic Speech Recognition (ASR) is essential for any voice-based application. The streaming capability of ASR becomes necessary to provide immediate feedback to the user in applications like Voice Search. LSTM/RNN and CTC based ASR systems are very simple to train and deploy for low latency streaming applications but have lower accuracy when compared to the state-of-the-art models. In this work, we build accurate LSTM, attention and CTC based streaming ASR models for large-scale Hinglish (blend of Hindi and English) Voice Search. We evaluate how various modifications in vanilla LSTM training improve the system’s accuracy while preserving the streaming capabilities. We also discuss a simple integration of end-of-speech (EOS) detection with CTC models, which helps reduce the overall search latency. Our model achieves a word error rate (WER) of 3.69% without EOS and 4.78% with EOS, with ~1300 ms (~46.64%) reduction in latency.</abstract>
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%0 Conference Proceedings
%T Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce
%A Goyal, Abhinav
%A Garera, Nikesh
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F goyal-garera-2023-building
%X Automatic Speech Recognition (ASR) is essential for any voice-based application. The streaming capability of ASR becomes necessary to provide immediate feedback to the user in applications like Voice Search. LSTM/RNN and CTC based ASR systems are very simple to train and deploy for low latency streaming applications but have lower accuracy when compared to the state-of-the-art models. In this work, we build accurate LSTM, attention and CTC based streaming ASR models for large-scale Hinglish (blend of Hindi and English) Voice Search. We evaluate how various modifications in vanilla LSTM training improve the system’s accuracy while preserving the streaming capabilities. We also discuss a simple integration of end-of-speech (EOS) detection with CTC models, which helps reduce the overall search latency. Our model achieves a word error rate (WER) of 3.69% without EOS and 4.78% with EOS, with ~1300 ms (~46.64%) reduction in latency.
%R 10.18653/v1/2023.acl-industry.26
%U https://aclanthology.org/2023.acl-industry.26
%U https://doi.org/10.18653/v1/2023.acl-industry.26
%P 276-283

Markdown (Informal)

[Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce](https://aclanthology.org/2023.acl-industry.26) (Goyal & Garera, ACL 2023)

• Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce (Goyal & Garera, ACL 2023)

ACL

• Abhinav Goyal and Nikesh Garera. 2023. Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 276–283, Toronto, Canada. Association for Computational Linguistics.