Training Speech Enhancement Systems with Noisy Speech Datasets

• URL: http://arxiv.org/abs/2105.12315v1
• Date: Wed, 26 May 2021 03:32:39 GMT
• Title: Training Speech Enhancement Systems with Noisy Speech Datasets
• Authors: Koichi Saito, Stefan Uhlich, Giorgio Fabbro, Yuki Mitsufuji
• Abstract summary: We propose two improvements to train SE systems on noisy speech data. First, we propose several modifications of the loss functions, which make them robust against noisy speech targets. We show that using our robust loss function improves PESQ by up to 0.19 compared to a system trained in the traditional way.
• Score: 7.157870452667369
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recently, deep neural network (DNN)-based speech enhancement (SE) systems have been used with great success. During training, such systems require clean speech data - ideally, in large quantity with a variety of acoustic conditions, many different speaker characteristics and for a given sampling rate (e.g., 48kHz for fullband SE). However, obtaining such clean speech data is not straightforward - especially, if only considering publicly available datasets. At the same time, a lot of material for automatic speech recognition (ASR) with the desired acoustic/speaker/sampling rate characteristics is publicly available except being clean, i.e., it also contains background noise as this is even often desired in order to have ASR systems that are noise-robust. Hence, using such data to train SE systems is not straightforward. In this paper, we propose two improvements to train SE systems on noisy speech data. First, we propose several modifications of the loss functions, which make them robust against noisy speech targets. In particular, computing the median over the sample axis before averaging over time-frequency bins allows to use such data. Furthermore, we propose a noise augmentation scheme for mixture-invariant training (MixIT), which allows using it also in such scenarios. For our experiments, we use the Mozilla Common Voice dataset and we show that using our robust loss function improves PESQ by up to 0.19 compared to a system trained in the traditional way. Similarly, for MixIT we can see an improvement of up to 0.27 in PESQ when using our proposed noise augmentation.

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