Frustratingly Easy Noise-aware Training of Acoustic Models

Desh Raj; Jesus Villalba; Daniel Povey; Sanjeev Khudanpur

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arXiv · 2011.02090

Frustratingly Easy Noise-aware Training of Acoustic Models

Desh Raj·Jesus Villalba·Daniel Povey·Sanjeev Khudanpur

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Abstract

Environmental noises and reverberation have a detrimental effect on the performance of automatic speech recognition (ASR) systems. Multi-condition training of neural network-based acoustic models is used to deal with this problem, but it requires many-folds data augmentation, resulting in increased training time. In this paper, we propose utterance-level noise vectors for noise-aware training of acoustic models in hybrid ASR. Our noise vectors are obtained by combining the means of speech frames and silence frames in the utterance, where the speech/silence labels may be obtained from a GMM-HMM model trained for ASR alignments, such that no extra computation is required beyond averaging of feature vectors. We show through experiments on AMI and Aurora-4 that this simple adaptation technique can result in 6-7% relative WER improvement. We implement several embedding-based adaptation baselines proposed in literature, and show that our method outperforms them on both the datasets. Finally, we extend our method to the online ASR setting by using frame-level maximum likelihood for the mean estimation.

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Desh Raj, Jesus Villalba, Daniel Povey, Sanjeev Khudanpur. 2020-11-04. Frustratingly Easy Noise-aware Training of Acoustic Models. https://arxiv.org/abs/2011.02090

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