Speaker verification using minimum classification error training

We propose a Minimum Verification Error (MVE) training scenario to design and adapt an HMM-based speaker verification system. By using the discriminative training paradigm, we show that customer and background models can be jointly estimated so that the expected number of verification errors (false accept and false reject) on the training corpus are minimized. An experimental evaluation of a fixed password speaker verification task over the telephone network was carried out. The evaluation shows that MVE training/adaptation performs as well as MLE training and MAP adaptation when performance is measured by average individual equal error rate (based on a posteriorithreshold assignment). After model adaptation, both approaches lead to an individual equal error-rate close to 0.6%. However, experiments performed with a priori dynamic threshold assignment show that MVE adapted models exhibit false rejection and false acceptance rates 45% lower than the MAP adapted models, and therefore lead to the design of a more robust system for practical applications