Approaches to automatic lexicon learning with limited training examples

Preparation of a lexicon for speech recognition systems can be a significant effort in languages where the written form is not exactly phonetic. On the other hand, in languages where the written form is quite phonetic, some common words are often mispronounced. In this paper, we use a combination of lexicon learning techniques to explore whether a lexicon can be learned when only a small lexicon is available for boot-strapping. We discover that for a phonetic language such as Spanish, it is possible to do that better than what is possible from generic rules or hand-crafted pronunciations. For a more complex language such as English, we find that it is still possible but with some loss of accuracy.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Nagendra Goel, Samuel Thomas, Mohit Agarwal, Pinar Akyazi, Lukas Burget, Kai Feng, Arnab Ghoshal, Ondrej Glembek, Martin Karafiát, Daniel Povey, Ariya Rastrow, Richard C. Rose, Petr Schwarz
• Keywords: ICASSP 2010, Lexicon, Signal Processing, Speech Recognition Systems, Written Form