Multi-Head State Space Model for Speech Recognition

• URL: http://arxiv.org/abs/2305.12498v2
• Date: Thu, 25 May 2023 21:55:58 GMT
• Title: Multi-Head State Space Model for Speech Recognition
• Authors: Yassir Fathullah, Chunyang Wu, Yuan Shangguan, Junteng Jia, Wenhan Xiong, Jay Mahadeokar, Chunxi Liu, Yangyang Shi, Ozlem Kalinli, Mike Seltzer, Mark J. F. Gales
• Abstract summary: State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks. In this paper, we propose a multi-head state space (MH-SSM) architecture equipped with special gating mechanisms. As a drop-in replacement for multi-head attention in transformer encoders, this new model significantly outperforms the transformer transducer on the LibriSpeech speech recognition corpus.
• Score: 44.04124537862432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches. In this paper, we propose a multi-head state space (MH-SSM) architecture equipped with special gating mechanisms, where parallel heads are taught to learn local and global temporal dynamics on sequence data. As a drop-in replacement for multi-head attention in transformer encoders, this new model significantly outperforms the transformer transducer on the LibriSpeech speech recognition corpus. Furthermore, we augment the transformer block with MH-SSMs layers, referred to as the Stateformer, achieving state-of-the-art performance on the LibriSpeech task, with word error rates of 1.76\%/4.37\% on the development and 1.91\%/4.36\% on the test sets without using an external language model.

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