1. Robust speech recognition based on independent vector analysis using harmonic frequency dependency.
- Author
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Jun, Soram, Kim, Minook, Oh, Myungwoo, and Park, Hyung-Min
- Subjects
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AUTOMATIC speech recognition , *VECTOR analysis , *ALGORITHMS , *ROBUST control , *BLIND source separation , *MACHINE learning , *CLUSTER analysis (Statistics) - Abstract
This paper describes an algorithm that enhances speech by independent vector analysis (IVA) using harmonic frequency dependency for robust speech recognition. While the conventional IVA exploits the full-band uniform dependencies of each source signal, a harmonic clique model is introduced to improve the enhancement performance by modeling strong dependencies among multiples of fundamental frequencies. An IVA-based learning algorithm is derived to consider the non-holonomic constraint and the minimal distortion principle to reduce the unavoidable distortion of IVA, and the minimum power distortionless response beamformer is used as a pre-processing step. In addition, the algorithm compares the log-spectral features of the enhanced speech and observed noisy speech to identify time-frequency segments corrupted by noise and restores those with the cluster-based missing feature reconstruction technique. Experimental results demonstrate that the proposed method enhances recognition performance significantly in noisy environments, especially with competing interference. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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