Paper Info
Title
Cepstral domain teager energy for identifying perceptually similar languages
Abstract
Language Identification (LID) refers to the task of identifying an unknown language from the test utterances. In this paper, a new feature set, viz., T-MFCC by amalgamating Teager Energy Operator (TEO) and well-known Mel frequency cepstral coefficients (MFCC) is developed. The effectiveness of the newly derived feature set is demonstrated for identifying perceptually similar Indian languages such as Hindi and Urdu. The modified structure of polynomial classifier of 2nd and 3rd order approximation has been used for the LID problem. The results have been compared with state-of-the art feature set, viz., MFCC and found to be effective (an average jump 21.66%) in majority of the cases. This may be due to the fact that the T-MFCC represents the combined effect of airflow properties in the vocal tract (which are known to be language and speaker dependent) and human perception process for hearing.
Year
DOI
Venue
2007
10.1007/978-3-540-77046-6_56
PReMI
Keywords
Field
DocType
perceptually similar indian language,feature set,airflow property,language identification,lid problem,cepstral domain teager energy,state-of-the art feature set,new feature set,average jump,unknown language,perceptually similar language,amalgamating teager energy operator,vocal tract,human perception,mel frequency cepstral coefficient
Mel-frequency cepstrum,Energy operator,Pattern recognition,Cepstral domain,Computer science,Hindi,Speech recognition,Speaker recognition,Artificial intelligence,Language identification,Perception,Vocal tract
Conference
Volume
ISSN
ISBN
4815
0302-9743
3-540-77045-3
Citations 
PageRank 
References 
1
0.42
5
Authors
2
Name
Order
Citations
PageRank
Hemant A. Patil116855.14
Tapan Kumar Basu2325.22