17 July 2000 Bispectrum analysis for speaker identification in a noisy environment with Karhunen-Loeve transformation technique
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Proceedings Volume 4044, Hybrid Image and Signal Processing VII; (2000); doi: 10.1117/12.391925
Event: AeroSense 2000, 2000, Orlando, FL, United States
The work described in this paper addresses the problem for extracting bispectrum feature of speech data. Very often the bispectrum feature extraction and data reduction are complicated due to some limiting constraints, i.e., no prior knowledge of feature's distribution and higher dimensionality of bispectrum data. In this article we developed an adaptive feature extraction mechanism based on cascade neural network in conjunction with feature's dimensionality reduction based on Karhunen-Loeve transformation technique. An adaptive codebook generation algorithm which is a cascade configuration of SOFM (Self Organizing Feature Map) and LVQ (Learning Vector Quantization) was used before the K-L transformation. The transformation was experimentally shown as an effective procedure for orthogonalization and dimensionality reduction of spectrum feature. Performance of our speaker identification system was perceived to be significantly increased even though using limited number of channels in noisy environment. We also tried to improve the capability of adaptive codebook generation algorithm by applying simplified differential competitive learning network.
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Benyamin Kusumoputro, Muhammad Ivan Fanany, Dian Indrawati, "Bispectrum analysis for speaker identification in a noisy environment with Karhunen-Loeve transformation technique", Proc. SPIE 4044, Hybrid Image and Signal Processing VII, (17 July 2000); doi: 10.1117/12.391925; https://doi.org/10.1117/12.391925


Feature extraction


Principal component analysis


Machine learning

Neural networks

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