Title
Non-linear mapping for multi-channel speech separation and robust overlapping spech recognition
Abstract
This paper investigates a non-linear mapping approach to extract robust features for ASR and speech separation of overlapping speech. Based on our previous studies, we continue to use two additional sound sources, namely from the target and interfering speakers. The focuses of this work are: 1) We investigate the feature mapping between different domains with the consideration of MMSE criterion and regression optimizations, demonstrating the mapping of log melfilterbank energies to MFCC can be exploited to improve the effectiveness of the regression; 2) We investigate the data-driven filtering for the speech separation by using the mapping method, which can be viewed as a generalized log spectral subtraction and results in better separation performance. We demonstrate the effectiveness of the proposed approach through extensive evaluations on the MONC corpus, which includes both non-overlapping single speaker and overlapping multi-speaker conditions.
Year
DOI
Venue
2009
10.1109/ICASSP.2009.4960485
ICASSP
Keywords
Field
DocType
Binary masking,Microphone array,Neural network,Overlapping speech recognition,Speech separatio
Speech processing,Mel-frequency cepstrum,Nonlinear system,Pattern recognition,Regression analysis,Computer science,Filter (signal processing),Microphone array,Speech recognition,Speaker recognition,Artificial intelligence,Artificial neural network
Conference
ISSN
Citations 
PageRank 
1520-6149
3
0.38
References 
Authors
10
4
Name
Order
Citations
PageRank
Weifeng Li113622.50
John Dines2986.79
Mathew Magimai-Doss351654.76
Herve Bourlard415237.75