Conclusion
Qualitative parameterization of time-frequency features for speaker recognition
Article REF: RE97 V1
Conclusion
Qualitative parameterization of time-frequency features for speaker recognition

Authors : Nidhal BEN ALOUI, Hervé GLOTIN, Patrick HEBRARD, Odile PAPINI

Publication date: February 10, 2009 | Lire en français

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6. Conclusion

A finer optimization should be obtained by combining the stamp value and using an HMM model for smoother estimates.

HMM: Hidden Markov Models

Our approach shows that this type of coding contains useful information for distinguishing speaker specificities, and we now need to integrate it into a tracking model that should provide complementary information to conventional models. (e.g. Gaussian model with cepstral parameters): SpkDet Mistral module .

The major difference with our qualitative approach lies in a representation for each speaker in a space of very small integers (the identity of 50 speakers is (partially) encoded by a set of QTF vectors each on [1:13] 15 ).

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