Evaluation of second language learners' pronunciation using hidden Markov models

In this study, Hidden Markov Models (HMMs) were used to evaluate pronunciation. Native and non-native speakers were asked to pronounce ten Dutch words. Each word was subsequently evaluated by an expert listener. Her main task was to decide whether a word was spoken by a native or a non-native speaker. For each word type, two versions of prototype HMMs were defined: one to be trained on tokens produced by a single native speaker, and another to be trained on tokens produced by a group of native speakers. For testing the different types of HMM, forced recognition was performed using native and non-native judged tokens. We expected that recognition with multispeaker HMMs would allow a more effective discrimination between native and non-native tokens than recognition with single-speaker models. A comparison of Equal Error Rates partly confirmed this hypothesis.