Isip 2000 Conversational Speech Evaluation System

The ISIP Public Domain Decoder for Large Vocabulary Conversational Speech Recognition

The BBN Byblos 2000 conversational Mandarin LVCSR system

This paper describes the year 2000 BBN Byblos Mandarin large vocabulary conversational speech recognition (LVCSR) system, the winning (and only) Mandarin system from the Spring 2000 Hub-5 evaluation sponsored by NIST. We first outline the training and decoding procedures used in the system, and describe the performance of the system used in the evaluation. We then describe the effect of several...

Recognizing Call-center Speech Using Models Trained from Other Domains

In this paper, we introduce a new conversational speech task – recognizing call-center speech – using data collected from Dragon’s own technical support line. We compare performance of models trained from conversational telephone speech (the Switchboard corpus) and models trained from predominantly read, microphone speech, and report on a series of experiments focusing on adapting the microphon...

Rate-of-speech Modeling for Large Vocabulary Conversational Speech Recognition

Variations in rate of speech (ROS) produce changes in both spectral features and word pronunciations that affect automatic speech recognition (ASR) systems. To deal with these ROS effects, we propose to use parallel, rate-specific, acoustic models: one for fast speech, the other for slow speech. Rate switching is permitted at word boundaries, to allow modeling within-sentence speech rate variat...

Rate-dependent Acoustic Modeling for Large Vocabulary Conversational Speech Recognition

Variations in rate of speech (ROS) produce changes in both spectral features and word pronunciations that affect automatic speech recognition (ASR) systems. To deal with these ROS effects, we propose to use parallel, rate-specific, acoustic models: one for fast speech, the other for slow speech. Rate switching is permitted at word boundaries, to allow modeling within-sentence speech rate variat...