NDLI: Modeling uncertainty of data observation

Content Provider	IEEE Xplore Digital Library
Author	Wendemuth, A.
Copyright Year	2001
Description	Author affiliation: Philips Res. Lab., Aachen, Germany (Wendemuth, A.)
Abstract	An approach is presented both theoretically and experimentally which overcomes a number of existing conceptual and performance problems in density estimation. The theoretical approach shows methods for incorporating or estimating uncertainties into speech recognition. In the maximum mutual information (MMI) and maximum likelihood (ML) case, precise formulae are given for estimation of densities for uncertainty variances small compared to the curvature of the posteriors. For implementation, the theoretical formulae are presented in such a way that the additional computation effort goes linearly with the number of densities. Experiments on car digits show relative improvements in word error rate of at most 4.8% relative. Uncertainty modelling is shown to help remedy effects of the sparse data problem in density estimation.
Sponsorship	IEEE Signal Process. Soc
Starting Page	501
Ending Page	504
File Size	338070
Page Count	4
File Format	PDF
ISBN	0780370414
ISSN	15206149
DOI	10.1109/ICASSP.2001.940877
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-05-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Speech recognition Maximum likelihood estimation Hidden Markov models Gaussian processes Laboratories Error analysis Mutual information Risk management Parametric statistics
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

Improved Semi-Parametric Mean Trajectory Model Using Discriminatively Trained Centroids

Discriminative training of hidden Markov models using a classification measure criterion

Boosted MMI for model and feature-space discriminative training

Maximum mutual information estimation of hidden Markov model parameters for speech recognition

Maximum mutual information speaker adapted training with semi-tied covariance matrices

Enhancing the discrimination of speaker independent hidden Markov models with corrective training

Phonetic recognition using hidden Markov models and maximum mutual information training

Hybrid modeling of PHMM and HMM for speech recognition

On maximum mutual information speaker-adapted training

Modeling uncertainty of data observation

Similar Documents

Improved Semi-Parametric Mean Trajectory Model Using Discriminatively Trained Centroids

Discriminative training of hidden Markov models using a classification measure criterion

Boosted MMI for model and feature-space discriminative training

Maximum mutual information estimation of hidden Markov model parameters for speech recognition

Maximum mutual information speaker adapted training with semi-tied covariance matrices

Enhancing the discrimination of speaker independent hidden Markov models with corrective training

Phonetic recognition using hidden Markov models and maximum mutual information training

Hybrid modeling of PHMM and HMM for speech recognition

On maximum mutual information speaker-adapted training

Modeling uncertainty of data observation