Please use this identifier to cite or link to this item: http://hdl.handle.net/11717/10674
Title: Automatic transcription of continuous speech into syllable-like units for Indian languages
Authors: Sarada, G.L.
Lakshmi, A.
Murthy, H.A.
Nagarajan, T.
Keywords: Automatic transcription
Continuous speech
Feature extraction techniques
Frame rate
Group delays
HMM models
Incremental training
Indian languages
Multiple frame sizes
Recognition performance
Speech segmentation
Speech signals
Syllable recognition
Training and testing
Unsupervised training
Feature extraction
Linguistics
Speech recognition
Clustering algorithms
Issue Date: 2009
Citation: Sadhana - Academy Proceedings in Engineering Sciences,34(2),221-233
Abstract: The focus of this paper is to automatically segment and label continuous speech signal into syllable-like units for Indian languages. In this approach, the continuous speech signal is first automatically segmented into syllable-like units using group delay based algorithm. Similar syllable segments are then grouped together using an unsupervised and incremental training (UIT) technique. Isolated style HMM models are generated for each of the clusters during training. During testing, the speech signal is segmented into syllable-like units which are then tested against the HMMs obtained during training. This results in a syllable recognition performance of 42·6% and 39·94% for Tamil and Telugu. A new feature extraction technique that uses features extracted from multiple frame sizes and frame rates during both training and testing is explored for the syllable recognition task. This results in a recognition performance of 48·7% and 45·36%, for Tamil and Telugu respectively. The performance of segmentation followed by labelling is superior to that of a flat start syllable recogniser (27·8%and 28·8%for Tamil and Telugu respectively).
URI: http://dx.doi.org/10.1007/s12046-009-0006-0
http://hdl.handle.net/11717/10674
ISSN: 2562499
Appears in Collections:Articles

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