NDLI: A semi-continuous stochastic trajectory model for phoneme-based continuous speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Siohan, O. Yifan Gong
Copyright Year	1996
Description	Author affiliation: CRIN, Vandoeuvre les Nancy, France (Siohan, O.)
Abstract	We propose a model of phoneme-based speech unit, called semi-continuous stochastic trajectory model (SC-STM), which generalizes our stochastic trajectory models (STM). As STMs, the SC-STMs focus on the modeling of speech segments (called trajectories) in their parameter space, and can therefore handle segmental information, which is critical for large vocabulary continuous speech recognition. Compared to the STMs, the SC-STMs improve the resolution of the trajectory modeling, while keeping a moderate number of free parameters by sharing state probability density functions. The SC-STM can therefore maintain a good trade-off between detailed acoustic modeling and limited training data. We tested the idea on a 2010 words, speaker-dependent, continuous speech database. Preliminary results show that SC-STM gives a word accuracy close to that of STM, without using heuristic techniques that enhanced STM.
Starting Page	471
Ending Page	474
File Size	375460
Page Count	4
File Format	PDF
ISBN	0780331923
ISSN	15206149
DOI	10.1109/ICASSP.1996.541135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-05-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Speech recognition Hidden Markov models Vocabulary Probability density function Context modeling Training data Acoustic testing Databases Solid modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A study on continuous Chinese speech recognition based on stochastic trajectory models

Phone recognition using hidden Markov models

Phoneme modelling using continuous mixture densities

Acoustic modeling by phoneme templates and modified one-pass DP decoding for continuous speech recognition

Acoustic training system for speaker independent continuous Arabic speech recognition system

Stochastic trajectory modeling for speech recognition

Large vocabulary word recognition based on demi-syllable hidden Markov model using small amount of training data

A time continuous model for speech recognition

A geometric interpretation of hidden markov model (1994).

A semi-continuous stochastic trajectory model for phoneme-based continuous speech recognition

Similar Documents

A study on continuous Chinese speech recognition based on stochastic trajectory models

Phone recognition using hidden Markov models

Phoneme modelling using continuous mixture densities

Acoustic modeling by phoneme templates and modified one-pass DP decoding for continuous speech recognition

Acoustic training system for speaker independent continuous Arabic speech recognition system

Stochastic trajectory modeling for speech recognition

Large vocabulary word recognition based on demi-syllable hidden Markov model using small amount of training data

A time continuous model for speech recognition

A geometric interpretation of hidden markov model (1994).

A semi-continuous stochastic trajectory model for phoneme-based continuous speech recognition