NDLI: Speech recognition using time-warping neural networks

Content Provider	IEEE Xplore Digital Library
Author	Aikawa, K.
Copyright Year	1991
Description	Author affiliation: NTT Human Interface Labs., Tokyo, Japan (Aikawa, K.)
Abstract	The author proposes a time-warping neural network (TWNN) for phoneme-based speech recognition. The TWNN is designed to accept phonemes with arbitrary duration, whereas conventional phoneme recognition networks have a fixed-length input window. The purpose of this network is to cope with not only variability of phoneme duration but also time warping in a phoneme. The proposed network is composed of several time-warping units which each have a time-warping function. The TWNN is characterized by time-warping functions embedded between the input layer and the first hidden layer in the network. The proposed network demonstrates higher phoneme recognition accuracy than a baseline recognizer based on conventional feedforward neural networks and linear time alignment. The recognition accuracy is even higher than that achieved with discrete hidden Markov models.
Starting Page	337
Ending Page	346
File Size	398198
Page Count	10
File Format	PDF
ISBN	0780301188
DOI	10.1109/NNSP.1991.239508
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-09-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Neural networks Feedforward systems Feedforward neural networks Hidden Markov models Feature extraction Dynamic programming Humans Laboratories Heuristic algorithms
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

Time-warping neural network for phoneme recognition

Quantized Dynamic Time Warping (DTW) algorithm

Introduction to Various Algorithms of Speech Recognition: Hidden Markov Model, Dynamic Time Warping and Artificial Neural Networks

Partial sequence matching using an Unbounded Dynamic Time Warping algorithm

Using SOMs as feature extractors for speech recognition

Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models

Monolingual and crosslingual comparison of tandem features derived from articulatory and phone MLPS

Connected and degraded text recognition using hidden Markov model

Biomimetic Pattern Recognition for Speaker-Independent Speech Recognition

Speech recognition using time-warping neural networks

Similar Documents

Time-warping neural network for phoneme recognition

Quantized Dynamic Time Warping (DTW) algorithm

Introduction to Various Algorithms of Speech Recognition: Hidden Markov Model, Dynamic Time Warping and Artificial Neural Networks

Partial sequence matching using an Unbounded Dynamic Time Warping algorithm

Using SOMs as feature extractors for speech recognition

Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models

Monolingual and crosslingual comparison of tandem features derived from articulatory and phone MLPS

Connected and degraded text recognition using hidden Markov model

Biomimetic Pattern Recognition for Speaker-Independent Speech Recognition

Speech recognition using time-warping neural networks