Automatic scoring of pronunciation quality

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 of human ratings. We then address pronunciation evaluation as a prediction problem, trying to predict the grade a human expert would assign to a particular skill. Using the speech and the expert-ratings databases, we build statistical models and introduce diierent machine scores that can be used as predictor variables. We validate these machine scores on the VILTS corpus, evaluating the pronunciation of American speakers speaking French, and we show that certain machine scores, like the log-posterior and the normalized duration, achieve a correlation with the targeted human grades that is comparable to the human-to-human correlation when a suucient amount of speech data is available.