TY - GEN
T1 - Exploiting Convolutional Neural Networks for Phonotactic Based Dialect Identification
AU - Najafian, Maryam
AU - Khurana, Sameer
AU - Shan, Suwon
AU - Ali, Ahmed
AU - Glass, James
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - In this paper, we investigate different approaches for Dialect Identification (DID) in Arabic broadcast speech. Dialects differ in their inventory of phonological segments. This paper proposes a new phonotactic based feature representation approach which enables discrimination among different occurrences of the same phone n-grams with different phone duration and probability statistics. To achieve further gain in accuracy we used multi-lingual phone recognizers, trained separately on Arabic, English, Czech, Hungarian and Russian languages. We use Support Vector Machines (SVMs), and Convolutional Neural Networks (CNN s) as backend classifiers throughout the study. The final system fusion results in 24.7% and 19.0% relative error rate reduction compared to that of a conventional phonotactic DID, and i-vectors with bottleneck features.
AB - In this paper, we investigate different approaches for Dialect Identification (DID) in Arabic broadcast speech. Dialects differ in their inventory of phonological segments. This paper proposes a new phonotactic based feature representation approach which enables discrimination among different occurrences of the same phone n-grams with different phone duration and probability statistics. To achieve further gain in accuracy we used multi-lingual phone recognizers, trained separately on Arabic, English, Czech, Hungarian and Russian languages. We use Support Vector Machines (SVMs), and Convolutional Neural Networks (CNN s) as backend classifiers throughout the study. The final system fusion results in 24.7% and 19.0% relative error rate reduction compared to that of a conventional phonotactic DID, and i-vectors with bottleneck features.
KW - CNN
KW - Dialect identification
KW - Phonotactics
UR - http://www.scopus.com/inward/record.url?scp=85054216672&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461486
DO - 10.1109/ICASSP.2018.8461486
M3 - Conference contribution
AN - SCOPUS:85054216672
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5174
EP - 5178
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -