Title
Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models
Abstract
AbstractWe investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy conditions within the deep neural network (DNN) framework. In contrast with recent results in the literature, we argue that a DNN vector-to-vector regression front-end for speech enhancement (DNN-SE) can play a key role in AAI when used to enhance spectral features prior to AAI back-end processing. We experimented with single- and multi-task training strategies for the DNN-SE block finding the latter to be beneficial to AAI. Furthermore, we show that coupling DNN-SE producing enhanced speech features with an AAI trained on clean speech outperforms a multi-condition AAI (AAI-MC) when tested on noisy speech. We observe a 15% relative improvement in the Pearson’s correlation coefficient (PCC) between our system and AAI-MC at 0 dB signal-to-noise ratio on the Haskins corpus. Our approach also compares favourably against using a conventional DSP approach to speech enhancement (MMSE with IMCRA) in the front-end. Finally, we demonstrate the utility of articulatory inversion in a downstream speech application. We report significant WER improvements on an automatic speech recognition task in mismatched conditions based on the Wall Street Journal corpus (WSJ) when leveraging articulatory information estimated by AAI-MC system over spectral-alone speech features.
Year
DOI
Venue
2022
10.1109/TASLP.2021.3133218
IEEE/ACM Transactions on Audio, Speech and Language Processing
Keywords
DocType
Volume
Noise measurement, Speech enhancement, Task analysis, Mel frequency cepstral coefficient, Training, Hidden Markov models, Deep learning, Deep neural network, acoustic-to-articulatory inversion, speech enhancement, multi-task training, speaker independent models
Journal
10.5555
Issue
ISSN
Citations 
taslp.2022.issue-30
2329-9290
0
PageRank 
References 
Authors
0.34
0
4