We are concerned with integrating connectionist networks into a hidden Markov model (HMM) speech recognition system. This is achieved through a statistical interpretation of connectionist networks as probability estimators. We review the basis of HMM speech recognition and point out the possible benefits of incorporating connectionist networks. Issues necessary to the construction of a connectionist HMM recognition system are discussed, including choice of connectionist probability estimator. We describe the performance of such a system using a multilayer perceptron probability estimator evaluated on the speaker-independent DARPA Resource Management database. In conclusion, we show that a connectionist component improves a state-of-the-art HMM system.

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SRI International’s EduSpeak® system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology. Automatic pronunciation scoring allows the computer to provide feedback on the overall quality of pronunciation and to point to specific production problems. We review our approach to pronunciation scoring, where our aim is to estimate the grade that a human expert would assign to the pronunciation quality of a paragraph or a phrase. Using databases of nonnative speech and corresponding human ratings at the sentence level, we evaluate different machine scores that can be used as predictor variables to estimate pronunciation quality. For more specific feedback on pronunciation, the EduSpeak toolkit supports a phone-level mispronunciation detection functionality that automatically flags specific phone segments that have been mispronounced. Phone-level information makes it possible to provide the student with feedback about specific pronunciation mistakes.Two approaches to mispronunciation detection were evaluated in a phonetically transcribed database of 130,000 phones uttered in continuous speech sentences by 206 nonnative speakers. Results show that classification error of the best system, for the phones that can be reliably transcribed, is only slightly higher than the average pairwise disagreement between the human transcribers.

SRI International is currently involved in the development of a new generation of software systems for automatic scoring of pronunciation as part of the Voice Interactive Language Training System (VILTS) project. This paper describes the goals of the VILTS system, the speech corpus, and the algorithm development. The automatic grading system uses SRI's Decipher™ continuous speech recognition system [1] to generate phonetic segmentations that are used to produce pronunciation scores at the end of each lesson. The scores produced by the system are similar to those of expert human listeners. Unlike previous approaches in which models were built for specific sentences or phrases, we present a new family of algorithms designed to perform well even when knowledge of the exact text to be used is not available.