'Acoustic models' diaporamas de présentation

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Speech Recognition

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Seminar Speech Recognition a Short Overview E.M. Bakker LIACS Media Lab Leiden University

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A Survey of Boosting HMM Acoustic Model Training

A Survey of Boosting HMM Acoustic Model Training. Introduction. The No Free Lunch Theorem states that There is no single learning algorithm that in any domain always induces the most accurate learner

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John Coleman Phonetics Laboratory, University of Oxford phon.ox.ac.uk/AudioBNC

Using speech technology, transcription standards and internet protocols to open up large-scale spoken audio collections. John Coleman Phonetics Laboratory, University of Oxford http://www.phon.ox.ac.uk/AudioBNC. NFAIS Annual Conference Sunday, 24th February 2013.

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Language Independent & Adaptive Speech Recognition

Language Independent & Adaptive Speech Recognition. Tanja Schultz Carnegie Mellon University Speech Seminar at Johns Hopkins University November, 27 2001. Motivation. Computerization : Speech processing is a key technology Internet and mobile systems Globalization : Multilinguality

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Seminar Speech Recognition Project Support E.M. Bakker LIACS Media Lab (LML) Leiden University

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Building an ASR using HTK CS4706

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Mandarin Pronunciation Variation Modeling

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Human Research Program Carl Walz

Human Research Program Carl Walz. September 17, 2007. Human Research Program Overview. Goals Reduce spaceflight risks to humans and focus on the highest risks to crew health and performance during exploration missions Enable development of human spaceflight medical and human factors standards

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Feb.21.2006 Rohit Kumar Affective Dialog Systems

Compensating for Hyperarticulation by Modeling Articulatory Properties Hagen Soltau, Florian Metze, Alex Waibel Interactions between Speech Recognition Problems and User Emotions Mihai Rotaru, Diane J. Litman, Kate Forbes-Riley. Feb.21.2006 Rohit Kumar Affective Dialog Systems.

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Sequential Modeling with the Hidden Markov Model

Sequential Modeling with the Hidden Markov Model. Lecture 9 Spoken Language Processing Prof. Andrew Rosenberg. Markov Assumption. If we can represent all of the information available in the present state, encoding the past is un-necessary.

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Using TDT Data to Improve BN Acoustic Models

Using TDT Data to Improve BN Acoustic Models. Long Nguyen and Bing Xiang STT Workshop Martigny, Switzerland, Sept. 5-6, 2003. Overview. TDT Data Selection procedure Data pre-processing Lightly-supervised decoding Selection Experimental results Conclusion & future work. TDT Data.

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Fundamentals of speech production and analysis

Fundamentals of speech production and analysis. Speech production and analysis: Web tutorium. Speech production Basic speech units: phoneme, syllable, word, phrase, sentence, speaking turn phone: subphonetic units, diphone, triphone, syllable as recognition units types of sounds:

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Broadcast News Training Experiments

Broadcast News Training Experiments. Anand Venkataraman, Dimitra Vergyri, Wen Wang, Ramana Rao Gadde, Martin Graciarena, Horacio Franco Jing Zheng and Andreas Stolcke Speech Technology & Research Laboratory SRI International, Menlo Park, CA. Goals.

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Acoustic models in Kaldi

Acoustic models in Kaldi . Acoustic models in Kaldi . Support for standard ML-trained models Linear transforms like LDA, HLDA, MLLT/STC Speaker adaptation with fMLLR, MLLR Support for tied-mixture systems initially discussed Support for SGMMs

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Innovative Strategies to Improve the Delivery of Telematics Voice Services

Innovative Strategies to Improve the Delivery of Telematics Voice Services. Julien Masson Director French Operations Connexis LLC. Consumers expectations for ubiquitous LBS. Consumers are more & more exposed to Navigation systems: Boom of the PND market

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Emerging Directions in Statistical Modeling in Speech Recognition

Emerging Directions in Statistical Modeling in Speech Recognition. Joseph Picone and Amir Harati Institute for Signal and Information Processing Temple University Philadelphia, Pennsylvania, USA. Abstract.

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