I. Lee Hetherington

JUPlTER: a telephone-based conversational interface for weather information

In early 1997, our group initiated a project to develop JUPITER, a conversational interface that allows users to obtain worldwide weather forecast information over the telephone using spoken dialogue. It has served as the primary research platform for our group on many issues related to human language technology, including telephone-based speech recognition, robust language understanding, language generation, dialogue modeling, and multilingual interfaces. Over a two year period since coming online in May 1997, JUPITER has received, via a toll-free number in North America, over 30000 calls (totaling over 180000 utterances), mostly from naive users. The purpose of this paper is to describe our development effort in terms of the underlying human language technologies as well as other system-related issues such as utterance rejection and content harvesting. We also present some evaluation results on the system and its components.

PRONUNCIATION MODELING USING A FINITE-STATE TRANSDUCER REPRESENTATION

Abstract The MIT summit speech recognition system models pronunciation using a phonemic baseform dictionary along with rewrite rules for modeling phonological variation and multi-word reductions. Each pronunciation component is encoded within a finite-state transducer (FST) representation whose transition weights can be trained using an EM algorithm for finite-state networks. This paper explains the modeling approach we use and the details of its realization. We demonstrate the benefits and weaknesses of the approach both conceptually and empirically using the recognizer for our jupiter weather information system. Our experiments demonstrate that the use of phonological rewrite rules within our system achieves word error rate reductions between 4% and 9% over different test sets when compared against a system using no phonological rewrite rules.

SAPPHIRE: an extensible speech analysis and recognition tool based on Tcl/Tk

The SAPPHIRE system is a powerful, extensible, object oriented toolkit allowing researchers to rapidly build and configure customized speech analysis tools. Implemented in Tcl/Tk and C, the current version of SAPPHIRE provides a wide range of functionality, including the ability to configure and run the SUMMIT speech recognition system. We use SAPPHIRE widely in almost all aspects of our speech analysis and recognition research.

Baum-Welch training for segment-based speech recognition

The use of segment-based features and segmentation networks in a segment-based speech recognizer complicates the probabilistic modeling because it alters the sample space of all possible segmentation paths and the feature observation space. This paper describes a novel Baum-Welch training algorithm for segment-based speech recognition which addresses these issues by an innovative use of finite-state transducers. This procedure has the desirable property of not requiring initial seed models that were needed by the Viterbi training procedure we have used previously. On the PhoneBook telephone-based corpus of read isolated words, the Baum-Welch training algorithm obtained a relative error reduction of 37 % on the training set and a relative error reduction of 5 % on the test set, compared to Viterbi trained models. When combined with a duration model, and more flexible segmentation network, the Baum-Welch trained models obtain an overall word error rate of 7.6 %, which is the best result we have seen published for the 8000 word task.