On the use of different Speech Representations for Speaker Modeling  

Oleh:

Ke Chen

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 35 no. 3 (Aug. 2005), page 301-314.
Topik: Different Speech Representations; Expectation Maximazation (EM) Algorithm; Generalized Gaussian Mixture Model (GGMM); KING Speech Corpus; Speaker Modelling; Speaker-Specific Information; Soft Competition; Speaker Recognition

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Numerous speech representations have been reported to be useful in speaker recognition. However, there is much less agreement on which speech representation provides a perfect representation of speaker-specific information conveyed in a speech signal. Unlike previous work, we propose an alternative approach to speaker modeling by the simultaneous use of different speech representations in an optimal way. Inspired by our previous empirical studies, we present a soft competition scheme on different speech representations to exploit different speech representations in encoding speaker-specific information. On the basis of this soft competition scheme, we present a parametric statistical model, generalized Gaussian mixture model (GGMM), to characterize a speaker identity based on different speech representations. Moreover, we develop an expectation-maximization algorithm for parameter estimation in the GGMM. The proposed speaker modeling approach has been applied to text-independent speaker recognition and comparative results on the KING speech corpus demonstrate its effectiveness.

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