Name
Title | ||
|---|---|---|
A Dnn Based Speech Enhancement Approach To Noise Robust Acoustic-To-Articulatory Inversion |
Abstract | ||
|---|---|---|
In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy condition within the deep neural network (DNN) framework. We claim that DNN vector-to-vector regression for speech enhancement (DNN-SE) can play a key role in AAI when used in a front-end stage to enhance speech features before AAI back-end processing. Our claim contrasts recent literature reporting a drop in AAI accuracy on MMSE enhanced data and thereby sheds some light on the opportunities offered by DNN-SE in robust speech applications. We have also tested single- and multi-task training strategies of the DNN-SE block and experimentally found the latter to be beneficial to AAI. Moreover, DNN-SE coupled with an AAI deep system tested on enhanced speech can outperform a multi-condition AAI deep system tested on noisy speech. We assess our approach on the Haskins corpus using the Pearson's correlation coefficient (PCC). A 15% relative PCC improvement is observed over a multi-condition AAI system at 0dB signal-to-noise ratio (SNR). Our approach also compares favorably against using a conventional DSP approach, namely MMSE with IMCRA, in the front-end stage. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401290 | 2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | DocType | ISSN |
Acoustic-to-articulatory inversion, DNN, speech enhancement | Conference | 0271-4302 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdolreza Sabzi Shahrebabaki | 1 | 1 | 3.41 |
| Sabato Marco Siniscalchi | 2 | 310 | 30.21 |
| Giampiero Salvi | 3 | 148 | 21.76 |
| Torbjørn Svendsen | 4 | 161 | 21.26 |