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» Canonical state models for automatic speech recognition
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LREC
2008
172views Education» more  LREC 2008»
14 years 11 months ago
CallSurf: Automatic Transcription, Indexing and Structuration of Call Center Conversational Speech for Knowledge Extraction and
Being the client's first interface, call centres worldwide contain a huge amount of information of all kind under the form of conversational speech. If accessible, this infor...
Martine Garnier-Rizet, Gilles Adda, Frederik Caill...
83
Voted
IPPS
1999
IEEE
15 years 1 months ago
A Parallel Phoneme Recognition Algorithm Based on Continuous Hidden Markov Model
This paper presents a parallel phoneme recognition algorithm based on the continuous Hidden Markov Model (HMM). The parallel phoneme recognition algorithm distributes 3-state HMMs...
Sang-Hwa Chung, Min-Uk Park, Hyung-Soon Kim
LREC
2008
138views Education» more  LREC 2008»
14 years 11 months ago
MISTRAL: a Statistical Machine Translation Decoder for Speech Recognition Lattices
This paper presents MISTRAL, an open source statistical machine translation decoder dedicated to spoken language translation. While typical machine translation systems take a writ...
Alexandre Patry, Philippe Langlais
INTERSPEECH
2010
14 years 4 months ago
SCARF: a segmental conditional random field toolkit for speech recognition
This paper describes a new toolkit - SCARF - for doing speech recognition with segmental conditional random fields. It is designed to allow for the integration of numerous, possib...
Geoffrey Zweig, Patrick Nguyen
ICASSP
2011
IEEE
14 years 1 months ago
Named entity recognition from Conversational Telephone Speech leveraging Word Confusion Networks for training and recognition
Named Entity (NE) recognition from the results of Automatic Speech Recognition (ASR) is challenging because of ASR errors. To detect NEs, one of the options is to use a statistica...
Gakuto Kurata, Nobuyasu Itoh, Masafumi Nishimura, ...