Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study
CoRR(2023)
摘要
Noise robustness is a key aspect of successful speech applications. Speech
enhancement (SE) has been investigated to improve automatic speech recognition
accuracy; however, its effectiveness for keyword spotting (KWS) is still
under-investigated. In this paper, we conduct a comprehensive study on
single-channel speech enhancement for keyword spotting on the Google Speech
Command (GSC) dataset. To investigate robustness to noise, the GSC dataset is
augmented with noise signals from the WSJ0 Hipster Ambient Mixtures (WHAM!)
noise dataset. Our investigation includes not only applying SE before KWS but
also performing joint training of the SE frontend and KWS backend models.
Moreover, we explore audio injection, a common approach to reduce distortions
by using a weighted average of the enhanced and original signals. Audio
injection is then further optimized by using another model that predicts the
weight for each utterance. Our investigation reveals that SE can improve KWS
accuracy on noisy speech when the backend model is trained on clean speech;
however, despite our extensive exploration, it is difficult to improve the KWS
accuracy with SE when the backend is trained on noisy speech.
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关键词
speech,enhancement,accuracy,single-channel
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