T
Timothy J. Hazen
Researcher at Microsoft
Publications - 82
Citations - 4875
Timothy J. Hazen is an academic researcher from Microsoft. The author has contributed to research in topics: Speaker recognition & Speech processing. The author has an hindex of 34, co-authored 82 publications receiving 4738 citations. Previous affiliations of Timothy J. Hazen include Johns Hopkins University & Bundelkhand University.
Papers
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Journal ArticleDOI
JUPlTER: a telephone-based conversational interface for weather information
Victor W. Zue,Stephanie Seneff,James Glass,Joseph Polifroni,Christine Pao,Timothy J. Hazen,Lee Hetherington +6 more
TL;DR: The purpose of this paper is to describe the development effort of JUPITER in terms of the underlying human language technologies as well as other system-related issues such as utterance rejection and content harvesting.
Journal ArticleDOI
Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error
TL;DR: This article reports significant gains in recognition performance and model compactness as a result of discriminative training based on MCE training applied to HMMs, in the context of three challenging large-vocabulary speech recognition tasks.
Proceedings ArticleDOI
Query-by-example spoken term detection using phonetic posteriorgram templates
TL;DR: A query-by-example approach to spoken term detection in audio files designed for low-resource situations in which limited or no in-domain training material is available and accurate word-based speech recognition capability is unavailable.
Journal ArticleDOI
Robust Speaker Recognition in Noisy Conditions
TL;DR: This paper describes a method that combines multicondition model training and missing-feature theory to model noise with unknown temporal-spectral characteristics, and is found to achieve lower error rates.
Journal ArticleDOI
Recognition confidence scoring and its use in speech understanding systems
TL;DR: This paper presents an approach to recognition confidence scoring and a set of techniques for integrating confidence scores into the understanding and dialogue components of a speech understanding system and demonstrates a relative reduction in concept error rate.