DISCRIMINATING OBSERVATION PROBABILITY (DOP) HMM FOR SPEAKER VERIFICATION
This paper describes the use of a multiple codebook semi-continuous hidden Markov model (SCHMM) automatic speaker verification (ASV) system, which uses a novel technique for discriminative hidden Markov modelling known as discriminative observation probabilities (DOP). DOP is not a discriminative training technique, it is a method of constructing what is effectively a discriminating model by contrasting two standard HMMs so as to improve discrimination between the classes that those models represent. This paper experimentally evaluates the use of DOP HMMs for ASV. The experimental evaluation is based on a text-dependent task using isolated digits. The database contains 24 true (client) speakers and 100 casual impostors, recorded over the public telephone network in the United Kingdom. For speaker verification, DOP has similarities to the so-called speaker normalisation technique but it is a more flexible approach and experimental results are presented here which show it to be superior, even in its simplest form. An argument is also made for preferring DOP models to discriminatively trained models although this is not evaluated experimentally. The DOP technique can easily be added to an existing HMM system and requires no additional training. The DOP concept can potentially be applied to other applications of HMMs to two-class classification problems. An equal error rate (EER) of 0.21% (using speaker specific thresholds, on a sequence of 12 isolated digits) was obtained using multiple codebook DOP models. This represents a reduction in EER of 38% compared to a common speaker normalisation technique.
discriminating observation probability (dop) speaker verification semicontinuous hidden marker models (hmm) speaker normalization cohort normalization telephone speech