Improved Modeling and Efficiency for Automatic Transcription of Broadcast News

Over the last few years, the DARPA-sponsored Hub-4 continuous speech recognition evaluations have advanced speech recognition technology for automatic transcription of broadcast news. In this paper, we report on our research and progress in this domain, with an emphasis on efficient modeling with significantly fewer parameters for faster and more accurate recognition. In the acoustic modeling area, this was achieved through new parameter tying, Gaussian clustering, and mixture weight thresholding schemes. The effectiveness of acoustic adaptation is greatly increased through unsupervised clustering of test data. In language modeling, we explored the use of non-broadcast-news training data, as well as adaptation to topic and speaking styles. We developed an effective and efficient parameter pruning technique for backoff language models that allowed us to cope with ever increasing amounts of training data and expanded N-gram scopes. Finally, we improved our progressive search architecture with more efficient algorithms for lattice generation, compaction, and incorporation of higher-order language models

Improved Modeling and Efficiency for Automatic Transcription of Broadcast News

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..