On the Complementarity of Phone Posterior Probabilities for Improved Speaker Recognition

In this letter, we apply Phone Log-Likelihood Ratio (PLLR) features to the task of speaker recognition. PLLRs, which are computed on the phone posterior probabilities provided by phone decoders, convey acoustic-phonetic information in a sequence of frame-level vectors, and therefore can be easily plugged into traditional acoustic systems, just by replacing the Mel-Frequency Cepstral Coefficients (MFCC) or an alternate representation. To study the performance of the proposed features, MFCC-based and PLLR-based systems are trained under an i-vector-PLDA approach. Results on the NIST 2010 and 2012 Speaker Recognition Evaluation databases show that, despite yielding lower performance than the acoustic system, the system based on PLLR features does provide significant gains when both systems are fused, which reveals a complementarity among features, and provides a suitable and effective way of using higher level phonetic information in speaker recognition systems.