Speaker Adaptive Training (SAT) has been shown to achieve significant word error reductions relative to the common Speaker Independent (SI) training paradigm, but its high requirements in disk I/O and space make it impractical for training on more than a couple hundred speakers. In the 1996 Hub-4 evaluation, the 38 hours of broadcast news training data consist of approximately 2000 speakers, half of them having less than 20 seconds of speech. In this paper we propose three implementations of SAT that are practical for training sets with a few thousands of speakers. First we present a two-pass SAT procedure that is mathematically equivalent to the original SAT method, with significantly reduced requirements in disk space, but essentially double the training time. Then we describe the Inverse Transform SAT (ITSAT) and the Least Squares SAT (LSSAT), two approximations to the SAT parameter estimation with time and space requirements that match those of common SI training. We show that the ...

Over the last few years, the DARPA-sponsored Hub4 continuous speech recognition evaluations have pushed speech recognition technology for the very interesting and difficult task of automatically transcribing broadcast news. In this paper, we report on our research and progress on this problem. We focus on individual techniques we developed, rather than on descriptions of our evaluation systems. We provide comparative experimental results showing the improvements obtained with the novel approaches we developed. 1 Introduction In recent years there has been increasing interest in developing large-vocabulary continuous speech recognition (LVCSR) systems for speech found in real sources. Broadcast news, in particular, has been the testbed for the DARPA-sponsored Hub4 continuous speech recognition (CSR) evaluations over the last few years, and represents a significant challenge to speech recognition researchers. Many interesting problems are associated with the automatic recognition of b...

This paper presents the CSLU Broadcast News transcription system used in the DARPA 1997 evaluation. The system was built using the softwares developed for the CSLU LVCSR project started in January 1997. This 25K-word vocabulary system used continuous HMMs for acoustic modeling and the standard backoff trigram as the language model. The search used a single pass decoder with MLLR based adaptation technique. Although on the standard DARPA 20k WSJ task our system obtained 11.6% word error, the 39% error on this year's evaluation suggests there are still many aspects need to be learned for a new comer like us. 1. Introduction This paper presents the CSLU Broadcast News transcription system used in the DARPA 1997 evaluation. The system was built using software developed for the CSLU LVCSR project, initiated in January 1997. The project proceeded through development and evaluation of systems associated with previous DARPA tasks; specifically the RM system, and the WSJ-5k and WSJ-20k system...

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