Abstract
The majority of deep neural network (DNN) based speech enhancement algorithms rely on the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has no apparent link to human perception, e.g., speech intelligibility. Short-time objective intelligibility (STOI), a popular state-of-the-art speech intelligibility estimator, on the other hand, relies on linear correlation of speech temporal envelopes. This raises the question if a DNN training criterion based on envelope linear correlation (ELC) can lead to improved speech intelligibility performance of DNN-based speech enhancement algorithms compared to algorithms based on the STSA-MSE criterion. In this paper, we derive that, under certain general conditions, the STSA-MSE and ELC criteria are practically equivalent, and we provide empirical data to support our theoretical results. Furthermore, our experimental findings suggest that the standard STSA minimum-MSE estimator is near optimal, if the objective is to enhance noisy speech in a manner, which is optimal with respect to the STOI speech intelligibility estimator.
Original language | English |
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Article number | 8509159 |
Journal | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Volume | 27 |
Issue number | 2 |
Pages (from-to) | 283-295 |
Number of pages | 13 |
ISSN | 2329-9290 |
DOIs | |
Publication status | Published - Feb 2019 |
Keywords
- Speech enhancement
- deep neural networks
- minimum mean-square error estimator
- speech intelligibility