8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


Context-Dependent Output Densities for Hidden Markov Models in Speech Recognition

Georg Stemmer, Viktor Zeissler, Christian Hacker, Elmar Nöth, Heinrich Niemann

Universität Erlangen-Nürnberg, Germany

In this paper we propose an efficient method to utilize context in the output densities of HMMs. State scores of a phone recognizer are integrated into the HMMs of a word recognizer which makes their output densities context-dependent. A significant reduction of the word error rate has been achieved when the approach is evaluated on a set of spontaneous speech utterances. As we can expect that context is more important for some phone models than for others, we further extend the approach by state-dependent weighting factors which are used to control the influence of the different information sources. A small additional improvement has been achieved.

Full Paper

Bibliographic reference.  Stemmer, Georg / Zeissler, Viktor / Hacker, Christian / Nöth, Elmar / Niemann, Heinrich (2003): "Context-dependent output densities for hidden Markov models in speech recognition", In EUROSPEECH-2003, 969-972.