Author: SRI International

This paper investigates the significance of combining cepstral features derived from the modified group delay function and from the short-time spectral magnitude like the MFCC.

We describe the development of the ICSI-SRI speech recognition system for the NIST Spring 2006 Meeting Rich Transcription (RT-06S) evaluation, highlighting improvements, including the delay-and-sum algorithm, the nearfield segmenter, language models, posterior-based features, HMM adaptation methods, and adapting to a small amount of new lecture data.

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.