A Speaker-Dependent Approach to Single-Channel Joint Speech Separation and Acoustic Modeling Based on Deep Neural Networks for Robust Recognition of Multi-Talker Speech

A Speaker-Dependent Approach to Single-Channel Joint Speech Separation and Acoustic Modeling... We propose a novel speaker-dependent (SD) multi-condition (MC) training approach to joint learning of deep neural networks (DNNs) of acoustic models and an explicit speech separation structure for recognition of multi-talker mixed speech in a single-channel setting. First, an MC acoustic modeling framework is established to train a SD-DNN model in multi-talker scenarios. Such a recognizer significantly reduces the decoding complexity and improves the recognition accuracy over those using speaker-independent DNN models with a complicated joint decoding structure assuming the speaker identities in mixed speech are known. In addition, a SD regression DNN for mapping the acoustic features of mixed speech to the speech features of a target speaker is jointly trained with the SD-DNN based acoustic models. Experimental results on Speech Separation Challenge (SSC) small-vocabulary recognition show that the proposed approach under multi-condition training achieves an average word error rate (WER) of 3.8%, yielding a relative WER reduction of 65.1% from a top performance, DNN-based pre-processing only approach we proposed earlier under clean-condition training (Tu et al. 2016). Furthermore, the proposed joint training DNN framework generates a relative WER reduction of 13.2% from state-of-the-art systems under multi-condition training. Finally, the effectiveness of the proposed approach is also verified on the Wall Street Journal (WSJ0) task with medium-vocabulary continuous speech recognition in a simulated multi-talker setting. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Signal Processing Systems Springer Journals

A Speaker-Dependent Approach to Single-Channel Joint Speech Separation and Acoustic Modeling Based on Deep Neural Networks for Robust Recognition of Multi-Talker Speech

Loading next page...
 
/lp/springer_journal/a-speaker-dependent-approach-to-single-channel-joint-speech-separation-TtcGa420FX
Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics
ISSN
1939-8018
eISSN
1939-8115
D.O.I.
10.1007/s11265-017-1295-x
Publisher site
See Article on Publisher Site

Abstract

We propose a novel speaker-dependent (SD) multi-condition (MC) training approach to joint learning of deep neural networks (DNNs) of acoustic models and an explicit speech separation structure for recognition of multi-talker mixed speech in a single-channel setting. First, an MC acoustic modeling framework is established to train a SD-DNN model in multi-talker scenarios. Such a recognizer significantly reduces the decoding complexity and improves the recognition accuracy over those using speaker-independent DNN models with a complicated joint decoding structure assuming the speaker identities in mixed speech are known. In addition, a SD regression DNN for mapping the acoustic features of mixed speech to the speech features of a target speaker is jointly trained with the SD-DNN based acoustic models. Experimental results on Speech Separation Challenge (SSC) small-vocabulary recognition show that the proposed approach under multi-condition training achieves an average word error rate (WER) of 3.8%, yielding a relative WER reduction of 65.1% from a top performance, DNN-based pre-processing only approach we proposed earlier under clean-condition training (Tu et al. 2016). Furthermore, the proposed joint training DNN framework generates a relative WER reduction of 13.2% from state-of-the-art systems under multi-condition training. Finally, the effectiveness of the proposed approach is also verified on the Wall Street Journal (WSJ0) task with medium-vocabulary continuous speech recognition in a simulated multi-talker setting.

Journal

Journal of Signal Processing SystemsSpringer Journals

Published: Oct 4, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off