Title
Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages
Abstract
In this paper, we conduct one of the very first studies for cross-corpora performance evaluation in the spoken language identification (LID) problem. Cross-corpora evaluation was not explored much in LID research, especially for the Indian languages. We have selected three Indian spoken language corpora: IIITH-ILSC, LDC South Asian, and IITKGP-MLILSC. For each of the corpus, LID systems are trained on the state-of-the-art time-delay neural network (TDNN) based architecture with MFCC features. We observe that the LID performance degrades drastically for cross-corpora evaluation. For example, the system trained on the IIITH-ILSC corpus shows an average EER of 11.80 % and 43.34 % when evaluated with the same corpora and LDC South Asian corpora, respectively. Our preliminary analysis shows the significant differences among these corpora in terms of mismatch in the long-term average spectrum (LTAS) and signal-to-noise ratio (SNR). Subsequently, we apply different feature level compensation methods to reduce the cross-corpora acoustic mismatch. Our results indicate that these feature normalization schemes can help to achieve promising LID performance on cross-corpora experiments.
Year
DOI
Venue
2021
10.23919/EUSIPCO54536.2021.9616273
29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021)
Keywords
DocType
ISSN
Cross-corpora, language recognition, channel compensation, long-term average spectrum, TDNN
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Spandan Dey100.34
Goutam Saha255.15
Md Sahidullah300.68