Search Results - "Wooters, Chuck"

Acoustic Beamforming for Speaker Diarization of Meetings by Anguera, X., Wooters, C., Hernando, J.

“…When performing speaker diarization on recordings from meetings, multiple microphones of different qualities are usually available and distributed around the…”
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Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information by Pardo, Jose, Anguera, Xavier, Wooters, Chuck

“…Human-machine interaction in meetings requires the localization and identification of the speakers interacting with the system as well as the recognition of…”
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Speaker Diarization Error Analysis Using Oracle Components by Huijbregts, M., van Leeuwen, D. A., Wooters, C.

“…In this paper, we describe an analysis of our speaker diarization system based on a series of oracle experiments. In this analysis, each system component is…”
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Acoustic beamforming for speaker diarization of meetings : Speaker and language recognition by ANGUERA, Xavier, WOOTERS, Chuck, HEMANDO, Javier

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The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource by Schneider, Nathan, Wooters, Chuck

“…A new Python API, integrated within the NLTK suite, offers access to the FrameNet 1.7 lexical database. The lexicon (structured in terms of frames) as well as…”
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Model Complexity Selection and Cross-Validation EM Training for Robust Speaker Diarization by Anguera, Xavier, Shinozaki, Takahiro, Wooters, Chuck, Hernando, Javier

“…Accurate modeling of speaker clusters is important in the task of speaker diarization. Creating accurate models involves both selection of the model complexity…”
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Automatic Weighting for the Combination of TDOA and Acoustic Features in Speaker Diarization for Meetings by Anguera, Xavier, Wooters, Chuck, Pardo, Jose M., Hernando, Javier

“…In the task of speaker diarization for meetings it has been shown in previous work that it is useful to use the time delay of arrival (TDOA) between the…”
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Voice activation of a surgical robotic assistant by Sackier, Jonathan M., Wooters, Chuck, Jacobs, Lisa, Halverson, Amy, Uecker, Darrin, Wang, Yulun

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Speech Recognition for Illiterate Access to Information and Technology by Plauche, M., Nallasamy, U., Pal, J., Wooters, C., Ramachandran, D.

“…In rural Tamil Nadu and other predominantly illiterate communities throughout the world, computers and technology are currently inaccessible without the help…”
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Speaker diarization for multi-party meetings using acoustic fusion by Anguera, X., Woofers, C., Hernando, J.

“…One of the sub-tasks of the Spring 2004 and Spring 2005 NIST Meetings evaluations requires segmenting multi-party meetings into speaker-homogeneous regions…”
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A fast-match approach for robust, faster than real-time speaker diarization by Yan Huang, Vinyals, O., Friedland, G., Muller, C., Mirghafori, N., Wooters, C.

“…During the past few years, speaker diarization has achieved satisfying accuracy in terms of speaker Diarization Error Rate (DER). The most successful…”
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Hybrid Speech/non-speech detector applied to Speaker Diarization of Meetings by Anguera, X., Aguilo, M., Wooters, C., Nadeu, C., Hernando, J.

“…When performing speaker diarization, it is common practice to use an agglomerative clustering approach where the acoustic data is first split in small segments…”
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CDNN: a context dependent neural network for continuous speech recognition by Bourlard, H., Morgan, N., Wooters, C., Renals, S.

“…A series of theoretical and experimental results have suggested that multilayer perceptrons (MLPs) are an effective family of algorithms for the smooth…”
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Connectionist acoustic word models by Wooters, C., Morgan, N.

“…Other researchers have claimed significant improvements to their recognizers by using word models based on data-driven subphonetic units rather than…”
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Experiments with temporal resolution for continuous speech recognition with multi-layer perceptrons by Morgan, N., Wooters, C., Hermansky, H.

“…Previous work by the authors focused on the integration of multilayer perceptrons (MLP) into hidden Markov models (HMM) and on the use of perceptual linear…”
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Speech segmentation and spoken document processing by Ostendorf, M., Favre, B., Grishman, R., Hakkani-Tur, D., Harper, M., Hillard, D., Hirschberg, J., Ji, H., Kahn, J.G., Liu, Y., Maskey, S., Matusov, E., Ney, H., Rosenberg, A., Shriberg, E., Wang, W., Wooters, C.

“…Progress in both speech and language processing has spurred efforts to support applications that rely on spoken rather than written language input. A key…”
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