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

IEEE International Conference on Acoustics Speech and Signal Processing, pp. 11331–11335

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.

Authors 4

1. Mohammad Zeineldeen Aachen Computer Science Department

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Machine Learning and Human Language Technology,Computer Science Department,Germany

   AppTek GmbH, Germany

   Computer Science Department, Machine Learning and Human Language Technology, RWTH Aachen University, Germany

2. Albert Zeyer Aachen Computer Science Department

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Machine Learning and Human Language Technology,Computer Science Department,Germany

   AppTek GmbH, Germany

   Computer Science Department, Machine Learning and Human Language Technology, RWTH Aachen University, Germany

3. Ralf Schlüter Aachen Computer Science Department

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Machine Learning and Human Language Technology,Computer Science Department,Germany

   AppTek GmbH, Germany

   Computer Science Department, Machine Learning and Human Language Technology, RWTH Aachen University, Germany

4. Hermann Ney Aachen Computer Science Department

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Machine Learning and Human Language Technology,Computer Science Department,Germany

   AppTek GmbH, Germany

   Computer Science Department, Machine Learning and Human Language Technology, RWTH Aachen University, Germany