Text independent speaker identification in multilingual environments I. Luengo, E. Navas, I. Sainz, I. Saratxaga, J. Sanchez, I. Odriozola and I. Hernaez

Contents Introduction  SR in language mismatched conditions  Existent solutions  Proposed solution Working database Variability measures Experimental results Conclusions

Speaker Recognition System Feature Extr. Train M Feature Extr. ScoreDecision TRAIN TEST Language mismatch? Accuracy decreases

Existent solutions Multi-language training  One model trained with various languages (per speaker)  Model learns characteristics of different languages Multi-model training  One model for each language (per speaker)  Language detector

Existent solutions Drawbacks  Possible languages must be known in advance for each speaker  Not generalizable for languages not seen during training  More recording sessions needed for training  + Time  + Money Desired solution: Language independent  Suitable for languages not seen during training  Capable of single-language training

Proposed solution Language-independent features NNormalization? NNew features? Short-term intonation and energy values High speaker discrimination capability Global distribution may change little with language Combinable with MFCC OOnly in voiced frames (intonation) HHigh session variability MMVN for inter-session normalization

Database Bilingual Spanish-Basque speech database  22 speakers (11 Male, 11 Female)  4 sessions (inter-session variability)  7 numeric sequences (8 digits) per session and language

Variability measures Adding new features ALWAYS increases separability/variability + Speaker separability  + discrimination  + Language variability  + model/test mismatch  + Session variability  + model/test mismatch Key issue: Does speaker separability increase more than language/session variability?

Variability measures Kullback-Leibler divergence for variability estimation Interesting measures:  Good if new features increase these ratios Inter-speaker variability Inter-language variability Inter-speaker variability Inter-session variability

Variability measures MFCCMFCC+PGain Lang % Spk S % B % Ses S % B % Spk/Lang S % B % Spk/Ses S % B %

Experimental results X-Y  Training in X, testing in Y S-SB-BS-BB-SSB-SSB-B MFCC (ref) MFCC (V) MFCC+P (V) Gain (V)-0.5% 13.4%9.0%-0.5%-1.3%

Conclusions Short-term intonation and energy values increase language robustness  Little accuracy drop on language-matched conditions Very useful if test language is unpredictable Variability measures predict results reasonably  Allows easy selection of features prior to experiments

Text independent speaker identification in multilingual environments I. Luengo, E. Navas, I. Sainz, I. Saratxaga, J. Sanchez, I. Odriozola and I. Hernaez