Browsing by Author "Bimbot, Frederic"

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    Effect of Utterance Duration and Phonetic Content on Speaker Identification Usind Second Order Statistical Methods
    (1995-01-01) Magrin-Chagnolleau, Ivan; Bonastre, Jean-Francois; Bimbot, Frederic; Digital Signal Processing (http://dsp.rice.edu/)
    Second-order statistical methods show very good results for automatic speaker identification in controlled recording conditions. These approaches are generally used on the entire speech material available. In this paper, we study the influence of the content of the test speech material on the performances of such methods, i.e. under a more analytical approach. The goal is to investigate on the kind of information which is used by these methods, and where it is located in the speech signal. Liquids and glides together, vowels, and more particularly nasal vowels and nasal consonants, are found to be particularly speaker specific: test utterances of 1 second, composed in majority of acoustic material from one of these classes provide better speaker identification results than phonetically balanced test utterances, even though the training is done, in both cases, with 15 seconds of phonetically balanced speech. Nevertheless, results with other phoneme classes are never dramatically poor. These results tend to show that the speaker-dependent information captured by long-term second-order statistics is consistently common to all phonetic classes, and that the homogeneity of the test material may improve the quality of the estimates.
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    A Further Investigation on AR-Vector Models for Text Independent Speaker Identification
    (1996-01-01) Magrin-Chagnolleau, Ivan; Bimbot, Frederic; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, we investigate on the role of dynamic information on the performances of AR-vector models for speaker recognition. To this purpose, we design an experimental protocol that destroys the time structure of speech frame sequences, which we compare to a more conventional one, i.e. keeping the natural time order. These results are also compared with those obtained with a (single) Gaussian model. Several measures are systematically investigated in the three cases, and different ways of symmetrisation are tested. We observe that the destruction of the time order can be a factor of improvement for the AR-vector models, and that results obtained with the Gaussian model are merely always better. In most cases, symmetrisation is beneficial.
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    Second-Order Statistical Measures for Text-Independent Speaker Identification
    (1995-08-20) Bimbot, Frederic; Magrin-Chagnolleau, Ivan; Digital Signal Processing (http://dsp.rice.edu/)
    This article presents an overview of several measures for speaker recognition. These measures relate to second-order statistical tests, and can be expressed under a common formalism. Alternate formulations of these measures are given and their mathematical properties are studied. In their basic form, these measures are asymmetric, but they can be symmetrized in various ways. All measures are tested in the framework of text-independent closed-set speaker identification, on 3 variants of the TIMIT database (630 speakers) : TIMIT (high quality speech), FTIMIT (a restricted bandwidth version of TIMIT) and NTIMIT (telephone quality). Remarkable performances are obtained on TIMIT but the results naturally deteriorate with FTIMIT and NTIMIT. Symmetrization appears to be a factor of improvement, especially when little speech material is available. The use of some of the proposed measures as a reference benchmark to evaluate the intrinsic complexity of a given database under a given protocol is finally suggested as a conclusion to this work.