NDLI: A left-to-right HDP-HMM with HDPM emissions

Content Provider	IEEE Xplore Digital Library
Author	Torbati, A.H.H.N. Picone, J. Sobel, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Temple Univ., Philadelphia, PA, USA (Torbati, A.H.H.N.; Picone, J.) \|\| Dept. of Stat., Temple Univ., Philadelphia, PA, USA (Sobel, M.)
Abstract	Nonparametric Bayesian models use a Bayesian framework to learn the model complexity automatically from the data and eliminate the need for a complex model selection process. The Hierarchical Dirichlet Process hidden Markov model (HDP-HMM) is the nonparametric Bayesian equivalent of an HMM. However, HDP-HMM is restricted to an ergodic topology and uses a Dirichlet Process Model (DPM) to achieve a mixture distribution-like model. For applications such as speech recognition, where we deal with ordered sequences, it is desirable to impose a left-to-right structure on the model to improve its ability to model the sequential nature of the speech signal. In this paper, we introduce three enhancements to HDP-HMM: (1) a left-to-right structure: needed for sequential decoding of speech, (2) non-emitting initial and final states: required for modeling finite length sequences, (3) HDP mixture emissions: allows sharing of data across states. The latter is particularly important for speech recognition because Gaussian mixture models have been very effective at modeling speaker variability. Further, due to the nature of language, some models occur infrequently and have a small number of data points associated with them, even for large corpora. Sharing allows these models to be estimated more accurately. We demonstrate that this new HDP-HMM model produces a 15% increase in likelihoods and a 15% relative reduction in error rate on a phoneme classification task based on the TIMIT Corpus.
Sponsorship	IEEE Inf. Theory Soc.
Starting Page	1
Ending Page	6
File Size	1149922
Page Count	6
File Format	PDF
ISBN	9781479930012
DOI	10.1109/CISS.2014.6814172
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	hidden Markov models speech recognition Computational modeling Hidden Markov models Speech recognition Speech Data models Bayes methods Topology non-parametric Bayesian models hierarchical Dirichlet processes
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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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