Author: SRI International

In this study we used the ICSI meeting corpus with high-level meeting recognition dialog act (MRDA) tags, that is, question, statement, backchannel, disruptions, and floor grabbers/holders. We performed controlled adaptation experiments using the Switchboard (SWBD) corpus with SWBD-DAMSL tags as the out-of-domain corpus.

This paper analyzes various methods to adapt sentence segmentation models trained on conversational telephone speech (CTS) to meeting style conversations. The sentence segmentation model trained using a large amount of CTS data is used to improve the performance when various amounts of meeting data are available.

We have constructed a hidden Markov model (HMM) system to detect sentence boundaries that uses both prosodic and textual information. Since there are more non-sentence boundaries than sentence boundaries in the data, the prosody model, which is implemented as a decision tree classifier, must be constructed to effectively learn from the imbalanced data distribution.

This paper describes a metadata detection system that combines information from different types of textual knowledge sources with information from a prosodic classifier. We investigate maximum entropy and conditional random field models, in addition to the predominant HMM approach, and find that discriminative models generally provide benefit over generative models.

An outline of the many machine translation research programs at SRI in 2006.