SPEECH & AUDIO
NLP
Multi-Head State Space Model for Speech Recognition
August 14, 2023
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.
AUTHORS
Written by
Yassir Fathullah
Chunyang Wu
Yuan Shangguan (June)
Wenhan Xiong
Jay Mahadeokar
Chunxi Liu
Mark Gales
Ozlem Kalinli
Publisher
Interspeech
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