In this paper, we describe a pronunciation lexicon model which is especially useful for constructing morpheme-based pronunciation lexicon to improve the performance of a Korean LVCSR. There are a lot of pronunciation variations occurring at morpheme boundaries in continuous speech. For modeling of cross-morpheme pronunciation variations, we usually used a context-dependent multiple pronunciatio...

We describe a pronunciation instruction system that dynamically generates CG animations to express the pronunciation from speech based on articulatory features. Specifically, the system displays the results of phoneme recognition and animation of the pronunciation movements of both the learner and teacher from their speech in order to show in what way the learner’s pronunciation is wrong. Learn...

FutureGrab is a new wearable musical instrument for live performance that is highly intuitive while still generating an interesting sound by subtractive synthesis. Its sound effects resemble the human vowel pronunciation, which were mapped to hand gestures that are similar to the mouth shape of human to pronounce corresponding vowel. FutureGrab also provides all necessary features for a lead mu...

The large pronunciation variability of words in conversational speech is one of the major causes of low accuracy for automatic speech recognition (ASR). Many pronunciation modeling approaches have been developed to address this problem. Some explicitly manipulate the pronunciation dictionary as well as the set of the units used to define the pronunciations of words. Others model the pronunciati...

In this paper, three different approaches to pronunciation modeling are investigated. Two existing pronunciation modeling approaches, namely the pronunciation dictionary and n-best rescoring approach are modified to work with little amount of non-native speech. We also propose a speaker clustering approach, which capable of grouping the speakers based on their pronunciation habits. Given some s...

Automated speech recognition systems work typically with pronunciation dictionary for generating expected phonetic content of particular words in recognized utterance. But the pronunciation can vary in many situations. Besides the cases with more possible pronunciation variants specified manually in the dictionary there are typically many other possible changes in the pronunciation depending on...

A lot of work has been done in deriving the pronunciation dictionary automatically from training data. These attempts focussed mainly on maximum likelihood or similar techniques. Due to the complexity and variability of the pronunciation process it is di cult to nd an adequate pronunciation model. The model will deviate from the truth. Hence, the application of maximum likelihood techniques is ...