Text-prompted speaker verification experiments with phoneme specific MLPs

The aims of the study described in this paper are (1) to assess the relative speaker discriminant properties of phonemes and (2) to investigate the importance of the temporal frame-to-frame information for speaker modelling in the framework of a text-prompted speaker verification system using hidden Markov models (HMMs) and multilayer perceptrons (MLPs). It is shown that, with similar experimental conditions, nasals, fricatives and vowels convey more speaker specific information than plosives and liquids. Regarding the influence of the frame-to-frame temporal information, significant improvements are reported from the inclusion of several acoustic frames at the input of the MLPs. The results tend also to show that each phoneme has its optimal MLP context size giving the best equal error rate (EER).

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[2]  J.M. Naik,et al.  Speaker verification: a tutorial , 1990, IEEE Communications Magazine.

[3]  H. Bourlard,et al.  Links Between Markov Models and Multilayer Perceptrons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  J. Oglesby,et al.  Optimisation of neural models for speaker identification , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[5]  Hiroaki Hattori,et al.  Text-independent speaker recognition using neural networks , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  C. Montacie,et al.  Cinematic techniques for speech processing: temporal decomposition and multivariate linear prediction , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Hervé Bourlard,et al.  Connectionist speech recognition , 1993 .

[8]  Sadaoki Furui,et al.  Concatenated phoneme models for text-variable speaker recognition , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Richard J. Mammone,et al.  Speaker recognition using neural networks and conventional classifiers , 1994, IEEE Trans. Speech Audio Process..

[10]  Jay M. Naik,et al.  A hybrid HMM-MLP speaker verification algorithm for telephone speech , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  J.P. Eatock,et al.  A quantitative assessment of the relative speaker discriminating properties of phonemes , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Hervé Bourlard,et al.  Connectionist probability estimators in HMM speech recognition , 1994, IEEE Trans. Speech Audio Process..

[13]  T. Artières.  Methodes predictives neuronales : application a l'identification du locuteur , 1995 .

[14]  Joachim Wilke,et al.  A further investigation on AR-vector models for text-independent speaker identification , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[15]  Jesper Ø. Olsen.  A Two Stage Procedure for Phone Based Speaker Verfication , 1997, AVBPA.

[16]  Jesper Ø. Olsen.  A two-stage procedure for phone based speaker verification , 1997, Pattern Recognit. Lett..