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Motivated by potential applications in second-language pedagogy, we present a novel approach to using articulatory information to improve automatic detection of typical phone-level errors made by nonnative speakers of English-a difficult task that involves discrimination between close pronunciations. We describe a reformulation of the hidden-articulator Markov model (HAMM) framework that is appropriate...
In this paper we describe an automatic prosody labeling framework that exploits both language and speech information intended for the purpose of incorporating prosody within a speech-to-speech translation framework. We propose a maximum entropy syntactic- prosodic model that achieves an accuracy of 85.22% and 91.54% for pitch accent and boundary tone labeling on the Boston University Radio News corpus...
Engineering automatic speech recognition (ASR) for speech to speech (S2S) translation systems, especially targeting languages and domains that do not have readily available spoken language resources, is immensely challenging due to a number of reasons. In addition to contending with the conventional data-hungry speech acoustic and language modeling needs, these designs have to accommodate varying...
The design of a robust language-learning system, intended to help students practice a foreign language along with a machine tutor, must provide for localization of common pronunciation errors. This paper presents a new technique for unsupervised detection of phone-level mispronunciations, created with language-learning applications in mind. Our method uses multiple hidden-articulator Markov models...
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