Speech Prosody 2008

Campinas, Brazil
May 6-9, 2008

Exploiting Prosodic Breaks in Language Modeling with Random Forests

Yi Su, Frederick Jelinek

Center for Language and Speech Processing, Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA

We propose a novel method of exploiting prosodic breaks in language modeling for automatic speech recognition (ASR) based on the random forest language model (RFLM), which is a collection of randomized decision tree language models and can potentially ask any questions about the history in order to predict the future. We demonstrate how questions about prosodic breaks can be easily incorporated into the RFLM and present two language models which treat prosodic breaks as observable and hidden variables, respectively. Meanwhile, we show empirically that a finer grained prosodic break is needed for language modeling. Experimental results showed that given prosodic breaks, we were able to reduce the LM perplexity by a significant margin, suggesting a prosodic N-best rescoring approach for ASR.

Full Paper

Bibliographic reference.  Su, Yi / Jelinek, Frederick (2008): "Exploiting prosodic breaks in language modeling with random forests", In SP-2008, 91-94.