Bilinear Time Series Model as an Alternative Way of Speaker Modeling
In the paper a class of non-linear time series models is considered, with respect to possible application for speaker recognition. Registered speech signal is is a non-stationary time series. This non-stationarity is usually modeled as autoregressive time series with time varying parameters. In the paper a bilinear approximation of non-stationary autoregressive model is proposed. This way, a model with time varying parameters is approximated by a constant parameters model. Parameters of the bilinear model are assumed to be the speaker features,and are applied for speaker recognition. Effectiveness of the proposed method is compared with classic methods of speaker recognition.
Keywordsnon-stationary AR models bilinear time series model speech analysis speaker recognition recognition
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- 3.Bielinska, E.M.: Bilinear representation of non-stationary autoregressive time series. In: Proceedings of the International Conference on System Science 2013. Advances in Systems Science, pp. 737–746. Springer, Heidelberg (2013)Google Scholar
- 4.Bensty, J., Sondhi, M., Huang, Y. (eds.): Springer Handbook of Speech Processing. Springer, Heidelberg (2007)Google Scholar
- 5.Campbell, J.: Speaker recognition: A Tutorial. Proceeding of the IEEE 85(9) (1977)Google Scholar
- 7.Kohlmorgen, J., Lemm, S.: An On-lLine method for segmentation and identification of non-stationary time series. In: Proceeding of Neural Networks for Signal Processing XI, pp. 113–122 (2001)Google Scholar
- 8.Ludwig, M.: Building on Durbin’s method to estimate MA processe. Improving Durbin’s method to estimate MA processes, arXiv:1304.7956, http://mludwig.org/research.html
- 10.Ozaki, T., Tong, H.: On moving average parameter estimation. In: Proceedings of the 8th Hawaii International Conference on System Science, pp. 224–226 (1995)Google Scholar
- 11.Pollock, D.S.G.: A Handbook of time series analysis, signal processing and dynamics. Academic Press (1999)Google Scholar
- 12.Rabiner, L.R.: Fundamentals of Speech Recognitions. Prentice Hall, New Jersey (1993)Google Scholar
- 13.Sandgren, N., Stoica, P., Babu, P.: On moving average parameter estimation. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 2348–2351 (2012)Google Scholar