NDLI: Sign language phoneme transcription with PCA-based representation

Content Provider	IEEE Xplore Digital Library
Author	Kong, W.W. Ranganath, S.
Copyright Year	2007
Description	Author affiliation: Nat. Univ. of Singapore, Singapore (Kong, W.W.; Ranganath, S.)
Abstract	A common approach to extract "phonemes" of sign language is to use an unsupervised clustering algorithm to group the sign segments. However, simple clustering algorithms based on distance measures usually do not work well on temporal data and require complex algorithms. This paper presents a simple and effective approach to extract phonemes from American sign language (ASL) sentences. We first apply a semi-automatic segmentation algorithm which detects minimal velocity and maximal change of directional angle to segment the hand motion trajectory of signed sentences. We then extract, feature descriptors based on principal component analysis (PCA) to represent the segments efficiently. These high level features are used with k-means to cluster the segments to form phonemes. 25 continuously signed sentences from a native signer are used to perform the analysis. After phoneme transcription, we train hidden Markov models (HMMs) to recognize the sequence of phonemes in the sentences. We compare the recognition results from HMMs when the phonemes are labeled by our algorithm, and when they are labeled manually. On the 25 test sentences containing 173 segments, the average number of errors obtained with our approach and the manual approach to labeling phonemes was 24.0 and 33.8, respectively.
Starting Page	1
Ending Page	5
File Size	162959
Page Count	5
File Format	PDF
ISBN	9781424409822
DOI	10.1109/ICICS.2007.4449647
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-10
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handicapped aids Clustering algorithms Data mining Change detection algorithms Hidden Markov models Principal component analysis Motion detection Feature extraction Performance analysis Testing
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

Automatic hand trajectory segmentation and phoneme transcription for sign language

Enhanced continuous sign language recognition using PCA and neural network features

A hybrid sign language recognition system

PCA feature extraction for change detection in multidimensional unlabelled streaming data

Computer Vision-based Bangladeshi Sign Language Recognition System

HMM-based motion recognition system using segmented PCA

Two Tier Feature Extractions for Recognition of Isolated Arabic Sign Language using Fisher's Linear Discriminants

Multilayer method based on multi-resolution feature extracting and MVC dimension reducing method for sign language recognition

Time Sensitive and Non-Time Sensitive Feature Extractions in Arabic Sign Language Recognition

Sign language phoneme transcription with PCA-based representation

Similar Documents

Automatic hand trajectory segmentation and phoneme transcription for sign language

Enhanced continuous sign language recognition using PCA and neural network features

A hybrid sign language recognition system

PCA feature extraction for change detection in multidimensional unlabelled streaming data

Computer Vision-based Bangladeshi Sign Language Recognition System

HMM-based motion recognition system using segmented PCA

Two Tier Feature Extractions for Recognition of Isolated Arabic Sign Language using Fisher's Linear Discriminants

Multilayer method based on multi-resolution feature extracting and MVC dimension reducing method for sign language recognition

Time Sensitive and Non-Time Sensitive Feature Extractions in Arabic Sign Language Recognition

Sign language phoneme transcription with PCA-based representation