Abstract
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.
Publication supported from the Human Capital Operational Programme co-financed by the European Union from the financial resources of the European Social Fund, project no. POKL.04.01.02-00-209/11.
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Kochana, O., Ksiazek, P., Olszak, M., Bielinska, E. (2014). Bilinear Time Series Model as an Alternative Way of Speaker Modeling. In: Zelinka, I., Suganthan, P., Chen, G., Snasel, V., Abraham, A., Rössler, O. (eds) Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-319-07401-6_23
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DOI: https://doi.org/10.1007/978-3-319-07401-6_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07400-9
Online ISBN: 978-3-319-07401-6
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