Abstract
In this paper, we propose efficient the number of computational iteration method of MNMF for speech recognition. The proposed method initializes estimates MNMF algorithm with the estimated spatial correlation matrix reduces the number of iteration of updates algorithm. The experiment result shows that our method reduced the computational complexity of MNMF.
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Acknowledgements
This work was supported by JSPS KAKENHI grant numbers 16K00245 and 15H02728.
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Izumi, T. et al. (2019). Reducing Computational Complexity of Multichannel Nonnegative Matrix Factorization Using Initial Value Setting for Speech Recognition. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_82
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DOI: https://doi.org/10.1007/978-3-319-93659-8_82
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Online ISBN: 978-3-319-93659-8
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