Jon P. Nedel

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Hidden Markov Models (HMMs) are known to model the duration of sound units poorly. In this paper we present a technique to normalize the duration of each phone to overcome this weakness, with the conjecture that speech with normalized phone durations may be better modeled and discriminated using standard HMM acoustic models. Duration normalization is(More)
When phone segmentations are known a priori, normalizing the duration of each phone has been shown to be effective in overcoming weaknesses in duration modeling of Hidden Markov Models (HMMs). While we have observed potential relative reductions in word error rate (WER) of up to 34.6% with oracle segmentation information, it has been difficult to achieve(More)
Spontaneous speech is highly variable and rarely conforms to conventional assumptions and linguistically defined pronunciation rules. Specifically, there may be many different continuous speech realizations for each expertly defined phonetic unit in the dictionary. The phones may be realized in a clean and complete fashion as in read speech, or they may be(More)
HMM-based large vocabulary speech recognition systems usually have a very large number of statistical parameters. For better estimation, the number of parameters is reduced by sharing them across models. The parameter sharing is decided by regression trees which are built using phonetic classes designed either by a human expert or by data-driven methods. In(More)
Amen! Blessing and glory and wisdom and thanksgiving and honor and power and might be to our God forever and ever! Amen. Abstract Accurate recognition of spontaneous speech is one of the most difficult problems in speech recognition today. When speech is produced in a carefully planned manner, automatic speech recognition (ASR) systems are very successful(More)
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