On the use of phone log-likelihood ratios as features in spoken language recognition

This paper presents an alternative feature set to the traditional MFCC-SDC used in acoustic approaches to Spoken Language Recognition: the log-likelihood ratios of phone posterior probabilities, hereafter Phone Log-Likelihood Ratios (PLLR), produced by a phone recognizer. In this work, an iVector sy...

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Bibliographic Details
Published in2012 IEEE Spoken Language Technology Workshop (SLT) pp. 274 - 279
Main Authors Diez, M., Varona, A., Penagarikano, M., Rodriguez-Fuentes, L. J., Bordel, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Acoustics
Computational modeling
Decoding
iVectors
Log-Likelihood Ratios
NIST
Phone Posterior Probabilities
Speech
Spoken Language Recognition
Training
Vectors
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Summary:This paper presents an alternative feature set to the traditional MFCC-SDC used in acoustic approaches to Spoken Language Recognition: the log-likelihood ratios of phone posterior probabilities, hereafter Phone Log-Likelihood Ratios (PLLR), produced by a phone recognizer. In this work, an iVector system trained on this set of features (plus dynamic coefficients) is evaluated and compared to (1) an acoustic iVector system (trained on the MFCC-SDC feature set) and (2) a phonotactic (Phone-lattice-SVM) system, using two different benchmarks: the NIST 2007 and 2009 LRE datasets. iVector systems trained on PLLR features proved to be competitive, reaching or even outperforming the MFCC-SDC-based iVector and the phonotactic systems. The fusion of the proposed approach with the acoustic and phonotactic systems provided even more significant improvements, outperforming state-of-the-art systems on both benchmarks.
ISBN:9781467351256
1467351253
DOI:10.1109/SLT.2012.6424235