Performance improvement of automatic speech recognition systems via multiple language models produced by sentence-based clustering

Grammar-based speech recognition systems exhibit performance degradation as their vocabulary sizes increase. Data clustering is deemed to reduce the proportionality of this problem. We introduce an approach to data clustering for automatic speech recognition systems using kohonen self-organized map. Clustering results are used further to build a language model for each of the clusters using CMU-Cambridge toolkit. The approach was implemented as a prototype for a large vocabulary and continuous speech recognition system and about 8% performance improvement was achieved in comparison with the performance achieved using the language model and dictionary provided by Sphinx3. We present the experimental results along with discussions, analysis and potential future directions.