Fuliang Weng

Word-level rate of speech modeling using rate-specific phones and pronunciations

Variations in rate of speech (ROS) produce changes in both spectral features and word pronunciations that affect ASR systems. To cope with these effects, we propose to use rate specific phone models and pronunciations for ROS modeling at the word level. Words are given three types of pronunciations fast, slow, and medium-consisting of rate-specific phone models, respectively. This approach allows us to model within sentence rate variation. To better model coarticulation effects, we introduce the concept, of zero-length phones, which enables short phones to be skipped without having to change their neighboring phones contexts. A data-driven approach is used to prune the pronunciation dictionary derived from rules for phone reduction. We tested these approaches on the Hub 4 database and achieved a relative improvement of 2.0% over the baseline-an evaluation-quality version of SRIs DECIPHER continuous speech recognition system-for clean native speech in the 1996 development set.

Improved modeling and efficiency for automatic transcription of Broadcast News

Abstract 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 the 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.

Handling compound nouns in a Swedish speech-understanding system

This paper describes and evaluates a simple and general solution to the handling of compound nouns in Swedish and other languages in which compounds can be formed by concatenation of single words. The basic idea is to split compounds into their components and treat these components as recognition units that are equivalent to other words in the language model. By using a principled grammar-based language-processing architecture, it is then possible to accommodate input in a split-compound format.