A Conformer Based Acoustic Model for Robust Automatic Speech Recognition

Abstract

This study addresses robust automatic speech recognition (ASR) by introducing a Conformer-based acoustic model. The proposed model builds on the wide residual bi-directional long short-term memory network (WRBN) with utterance-wise dropout and iterative speaker adaptation, but employs a Conformer encoder instead of the recurrent network. The Conformer encoder uses a convolution-augmented attention mechanism for acoustic modeling. The proposed system is evaluated on the monaural ASR task of the CHiME-4 corpus. Coupled with utterance-wise normalization and speaker adaptation, our model achieves 6.25\% word error rate, which outperforms WRBN by 8.4\% relatively. In addition, the proposed Conformer-based model is 18.3\% smaller in model size and reduces total training time by 79.6\%.

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Reproductions