Horacio Franco

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This work is part of an effort aimed at developing computerbased systems for language instruction; we address the task of grading the pronunciation quality of the speech of a student of a foreign language. The automatic grading system uses SRI’s DecipherTM continuous speech recognition system to generate phonetic segmentations. Based on these segmentations(More)
We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected, and a database of human-expert ratings is created to enable the development of a variety of machine scores. We rst discuss issues related to the design of speech databases, and the reliability(More)
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(More)
The aim of the work described in this paper is to develop methods for automatically assessing the pronunciation quality of specific phone segments uttered by students learning a foreign language. From the phonetic time alignments generated by SRI’s DecipherTM HMM-based speech recognition system, we use various probabilistic models to produce pronunciation(More)
This work is part of an effort aimed at developing computer-based systems for language instruction; we address the task of grading the pronunciation quality of the speech of a student of a foreign language. The automatic grading system uses SRI’s DecipherTM continuous speech recognition system to generate phonetic segmentations. Based on these segmentations(More)
In a speaker-independent, large-vocabulary continuous speech recognition systems, recognition accuracy varies considerably from speaker to speaker, and performance may be significantly degraded for outlier speakers such as nonnative talkers. In this paper, we explore supervised speaker adaptation and normalization in the MLP component of a hybrid hidden(More)
We summarize recent progress in automatic speech-to-text transcription at SRI, ICSI, and the University of Washington. The work encompasses all components of speech modeling found in a state-of-the-art recognition system, from acoustic features, to acoustic modeling and adaptation, to language modeling. In the front end, we experimented with nonstandard(More)
The 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. We first report results on the application of adaptation techniques to recognize both native and nonnative speech in a speaker-independent manner. We discuss(More)
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(More)
Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated(More)