Spatial correlation model based observation vector clustering and MVDR beamforming for meeting recognition

This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estim...

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Bibliographic Details
Main Authors: Araki, Shoko, Okada, Masahiro, Higuchi, Takuya, Ogawa, Atsunori, Nakatani, Tomohiro
Format: Conference Proceeding
Language:English
Subjects:
Array signal processing
Beamforming
Clustering
complex Gaussian mixture model (CGMM)
Correlation
di-arization
Mathematical analysis
meeting speech recognition
Meetings
Microphones
Minimum variance distortionless response (MVDR)
Speech
Speech enhancement
Speech processing
Speech recognition
Steering
Time-frequency analysis
Vectors (mathematics)
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Summary:This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estimation of the steering vector is paramount. We recently found that steering vector estimation by clustering the time-frequency components of microphone observation vectors performs well as regards real-world noise reduction. The clustering is performed by taking a cue from the spatial correlation matrix of each speaker, which is realized by modeling the time-frequency components of the observation vectors with a complex Gaussian mixture model (CGMM). Experimental results with real recordings show that the proposed MVDR scheme outperforms conventional null-beamformer based speech enhancement in a meeting situation.
ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7471702