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Robust speech recognition based on independent vector analysis using harmonic frequency dependency

  • ICONIP 2011
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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.

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Acknowledgments

This work was supported by the Mid-career Researcher Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology of Korea (No. 2011-0027537). We appreciate valuable comments and advice of Il-Young Jeong.

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Authors and Affiliations

  1. Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro(Sinsu-dong), Mapo-gu, Seoul, 121-742, Republic of Korea

    Soram Jun, Minook Kim, Myungwoo Oh & Hyung-Min Park

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  1. Soram Jun
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  2. Minook Kim
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  3. Myungwoo Oh
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  4. Hyung-Min Park
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Correspondence to Hyung-Min Park.

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Jun, S., Kim, M., Oh, M. et al. Robust speech recognition based on independent vector analysis using harmonic frequency dependency. Neural Comput & Applic 22, 1321–1327 (2013). https://doi.org/10.1007/s00521-012-1002-6

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  • DOI: https://doi.org/10.1007/s00521-012-1002-6

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