Unsupervised pronunciation validation

This paper addresses selecting between candidate pronunciations for out-of-vocabulary words in speech processing tasks. We introduce a simple, unsupervised method that outperforms the conventional supervised method of forced alignment with a reference. The success of this method is independently demonstrated using three metrics from largescale speech tasks: word error rates for large vocabulary continuous speech recognition, decision error tradeoff curves for spoken term detection, and phone error rates compared to a handcrafted pronunciation lexicon. The experiments were conducted using state-of-the-art recognition, indexing, and retrieval systems. The results were compared across many terms, hundreds of hours of speech, and well known data sets.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Christopher M. White, Abhinav Sethy, Bhuvana Ramabhadran, Patrick Wolfe, Erica Cooper, Murat Saraclar, James K. Baker
• Keywords: Error Rates, ICASSP 2009, Largescale Speech Tasks, Signal Processing, Speech Processing Tasks