In this paper we propose the inversion of nonlinear distortions in
order to improve the recognition rates of a speaker recognizer system. We
study the effect of saturations on the test signals, trying to take into account real
situations where the training material has been recorded in a controlled
situation but the testing ...»»»»
In this paper we propose the inversion of nonlinear distortions in
order to improve the recognition rates of a speaker recognizer system. We
study the effect of saturations on the test signals, trying to take into account real
situations where the training material has been recorded in a controlled
situation but the testing signals present some mismatch with the input signal
level (saturations). The experimental results for speaker recognition shows that
a combination of several strategies can improve the recognition rates with
saturated test sentences from 80% to 89.39%, while the results with clean
speech (without saturation) is 87.76% for one microphone, and for speaker
identification can reduce the minimum detection cost function with saturated
test sentences from 6.42% to 4.15%, while the results with clean speech
(without saturation) is 5.74% for one microphone and 7.02% for the other one.^^^^
Tipus:
Article
Indexació:
Indexat a SCOPUS
Indexat a WOS/JCR
Drets:
(c) Elsevier, 2006
Tots els drets reservats
Citació Bibliogràfica:
Solé i Casals, Jordi ; Marcos Faúndez-Zanuy. "Application of the mutual information minimization to speaker recognition/identification improvement". A: Neurocomputing, 2006, 69(13-15): 1467-1474