Publications

We describe an algorithm based on acoustic clustering and acoustic adaptation to significantly improve speech recognition performance. The method is particularly useful when speech from multiple speakers is to be…

We show that the standard hypothesis scoring paradigm used in maximum-likelihood-based speech recognition systems is not optimal with regard to minimizing the word error rate, the commonly used performance metric…

In this paper, we propose a new algorithm to train mixtures of transformation networks (MTNs) in the hybrid connectionist recognition framework. We apply the new algorithm to nonnative speaker adaptation,…

We present a maximum entropy language model that incorporates both syntax and semantics via a dependency grammar.

In this article, we use our interaction model to demonstrate that during multimodal fusion, speech should be a privileged modality, driving the interpretation of a query, and that in certain…

This paper describes our work in developing multilingual (Swedish and English) speech recognition systems in the ATIS domain. The acoustic component of the multilingual systems is realized through sharing Gaussian…

We present a novel approach to hidden Markov model (HMM) state clustering based on the use of broad phone classes and an allophone class entropy measure. Our algorithm allows clustering…

In this paper we show that we can significantly improve HMM- based scores by using average phone segment posterior probabilities. Correlation between machine and human scores went up from r=0.50…

In the context of automatic speaker recognition, we propose a model transformation technique that renders speaker models more robust to acoustic mismatches and to data scarcity by appropriately increasing their…

This paper proposes a probabilstic framework to define and evaluate confidence measures for word recognition. We describe a novel method to combine different knowledge sources and estimate the confidence in…