Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition

• URL: http://arxiv.org/abs/2309.08436v2
• Date: Wed, 17 Jan 2024 11:28:30 GMT
• Title: Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition
• Authors: Mohammad Zeineldeen and Albert Zeyer and Ralf Schl\"uter and Hermann Ney
• Abstract summary: We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks. A special end-of-chunk symbol advances from one chunk to the next chunk, effectively replacing the conventional end-of-sequence symbol. We find that our model maintains competitive performance compared to the non-streamable variant and generalizes very well to long-form speech.
• Score: 42.04873382667665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks. A special end-of-chunk (EOC) symbol advances from one chunk to the next chunk, effectively replacing the conventional end-of-sequence symbol. This modification, while minor, situates our model as equivalent to a transducer model that operates on chunks instead of frames, where EOC corresponds to the blank symbol. We further explore the remaining differences between a standard transducer and our model. Additionally, we examine relevant aspects such as long-form speech generalization, beam size, and length normalization. Through experiments on Librispeech and TED-LIUM-v2, and by concatenating consecutive sequences for long-form trials, we find that our streamable model maintains competitive performance compared to the non-streamable variant and generalizes very well to long-form speech.

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