The goal of this workshop project is to explore language modeling techniques to improve recognition of unrestricted, conversational Spanish, over telephone channels. The basic training and test data will be from the Spanish language component of the Linguistic Data Consortium’s Call Home corpus. This is a corpus of transcribed telephone conversations. Text corpora, as well as other sources of transcribed speech, will be available.
We will be starting with a baseline Spanish speech recognizer built with BBN’s Byblos speech recognition system. The workshop will be provided with N-best and/or lattice outputs from this recognizer. We will endeavor to develop and evaluate language models for improving on the baseline performance level. In particular it will be desirable to exploit specific aspects of the Spanish language to improve the performance of the recognizer. The N-best lists and lattices will provide one means for evaluating our ideas and perplexity measurements another. Our progress will also be measured by our improved understanding of how language characteristics should influence our choice of a language model for recognition.
|Carol Van Ess-Dykema|