NDLI: Resource configurable spoken query detection using Deep Boltzmann Machines

Content Provider	IEEE Xplore Digital Library
Author	Yaodong Zhang Salakhutdinov, R. Hung-An Chang Glass, J.
Copyright Year	2012
Description	Author affiliation: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA (Yaodong Zhang; Hung-An Chang; Glass, J.) \|\| Department of Statistics, University of Toronto, Ontario, Canada (Salakhutdinov, R.)
Abstract	In this paper we present a spoken query detection method based on posteriorgrams generated from Deep Boltzmann Machines (DBMs). The proposed method can be deployed in both semi-supervised and unsupervised training scenarios. The DBM-based posteriorgrams were evaluated on a series of keyword spotting tasks using the TIMIT speech corpus. In unsupervised training conditions, the DBM-approach improved upon our previous best unsupervised keyword detection performance using Gaussian mixture model-based posteriorgrams by over 10%. When limited amounts of labeled data were incorporated into training, the DBM-approach required less than one third of the annotated data in order to achieve a comparable performance of a system that used all of the annotated data for training.
Starting Page	5161
Ending Page	5164
File Size	100912
Page Count	4
File Format	PDF
ISBN	9781467300452
ISSN	15206149
e-ISBN	9781467300469
e-ISBN	9781467300445
DOI	10.1109/ICASSP.2012.6289082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-25
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Speech Vectors Speech recognition Data models Probability Machine learning Deep Boltzmann Machines spoken query detection posteriorgram
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

Resource configurable spoken query detection using deep Boltzmann machines (2012)

Resource configurable spoken query detection using deep Boltzmann machines (2012)

An acoustic segment modeling approach to query-by-example spoken term detection

Gender aware Deep Boltzmann Machines for phone recognition

Low-resource language recognition using a fusion of phoneme posteriorgram counts, acoustic and glottal-based i-vectors

Saliency detection based on feature learning using Deep Boltzmann Machines

Investigation of deep Boltzmann machines for phone recognition

Acoustic emotion recognition based on fusion of multiple feature-dependent deep Boltzmann machines

Query-by-example spoken term detection using phonetic posteriorgram templates

Resource configurable spoken query detection using Deep Boltzmann Machines

Similar Documents

Resource configurable spoken query detection using deep Boltzmann machines (2012)

Resource configurable spoken query detection using deep Boltzmann machines (2012)

An acoustic segment modeling approach to query-by-example spoken term detection

Gender aware Deep Boltzmann Machines for phone recognition

Low-resource language recognition using a fusion of phoneme posteriorgram counts, acoustic and glottal-based i-vectors

Saliency detection based on feature learning using Deep Boltzmann Machines

Investigation of deep Boltzmann machines for phone recognition

Acoustic emotion recognition based on fusion of multiple feature-dependent deep Boltzmann machines

Query-by-example spoken term detection using phonetic posteriorgram templates

Resource configurable spoken query detection using Deep Boltzmann Machines