'Viterbi search' diaporamas de présentation

Sphinx-3 to 3.2

Sphinx-3 to 3.2. Mosur Ravishankar School of Computer Science, CMU Nov 19, 1999. Outline. Recognition problem Search for the most likely word sequence matching the input speech, given the various models Illustrated using Sphinx-3 (original) Lextree search (Sphinx-3.2) Search organization

601 views • 36 slides

Sphinx-3 to 3.2

Mosur Ravishankar School of Computer Science, CMU Nov 19, 1999. Sphinx-3 to 3.2. Outline. Recognition problem Search for the most likely word sequence matching the input speech, given the various models Illustrated using Sphinx-3 (original) Lextree search (Sphinx-3.2) Search organization

525 views • 36 slides

Sphinx-3 to 3.2

Sphinx-3 to 3.2. Mosur Ravishankar School of Computer Science, CMU Nov 19, 1999. Outline. Recognition problem Search for the most likely word sequence matching the input speech, given the various models Illustrated using Sphinx-3 (original) Lextree search (Sphinx-3.2) Search organization

551 views • 36 slides

8.0 Search Algorithms for Speech Recognition

362 views • 23 slides

8.0 Search Algorithms for Speech Recognition

344 views • 23 slides

8.0 Search Algorithms for Speech Recognition

344 views • 23 slides

Exploiting diverse knowledge source via Maximum Entropy in Name Entity Recognition

Exploiting diverse knowledge source via Maximum Entropy in Name Entity Recognition. Author: Andrew Borthwick John Sterling Eugene Agichtein Ralph Grishman Speaker: Shasha Liao. Content. Name Entity Recognition (NER) Maximum Entropy (ME)

290 views • 15 slides

Exploiting diverse knowledge source via Maximum Entropy in Name Entity Recognition

Exploiting diverse knowledge source via Maximum Entropy in Name Entity Recognition. Author: Andrew Borthwick John Sterling Eugene Agichtein Ralph Grishman Speaker: Shasha Liao. Content. Name Entity Recognition (NER) Maximum Entropy (ME)

153 views • 15 slides

On Use of Temporal Dynamics of Speech for Language Identification

On Use of Temporal Dynamics of Speech for Language Identification. Andre Adami Pavel Matejka Petr Schwarz Hynek Hermansky Anthropic Signal Processing Group http://www.asp.ogi.edu. Target language model. (+). Speech signal. Segmentation. units.

293 views • 14 slides

Spelling correction as an iterative process that exploits the collective knowledge of web users

Spelling correction as an iterative process that exploits the collective knowledge of web users. Silviu Cucerzan and Eric Brill. July, 2004. Speaker: Mengzhe Li. Spell Checking of Search Engine Queries. Traditional Word P rocessing S pell C hecker: Resolve typographical errors

263 views • 12 slides