Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
arxiv(2024)
摘要
In this work, we propose Mel-FullSubNet, a single-channel Mel-spectrogram
denoising and dereverberation network for improving both speech quality and
automatic speech recognition (ASR) performance. Mel-FullSubNet takes as input
the noisy and reverberant Mel-spectrogram and predicts the corresponding clean
Mel-spectrogram. The enhanced Mel-spectrogram can be either transformed to
speech waveform with a neural vocoder or directly used for ASR. Mel-FullSubNet
encapsulates interleaved full-band and sub-band networks, for learning the
full-band spectral pattern of signals and the sub-band/narrow-band properties
of signals, respectively. Compared to linear-frequency domain or time-domain
speech enhancement, the major advantage of Mel-spectrogram enhancement is that
Mel-frequency presents speech in a more compact way and thus is easier to
learn, which will benefit both speech quality and ASR. Experimental results
demonstrate a significant improvement in both speech quality and ASR
performance achieved by the proposed model.
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