TY - GEN
T1 - On the performance of ensemble-based classifiers for Arabic speech recognition
AU - Abo Absa, Ahmed H.
AU - Deriche, Mohamed
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Speech recognition continues to be a challenging research problem for diverse applications. The challenge is to develop better recognition systems, more robust, computationally more efficient, and versatile in nature. While western and eastern languages have attracted a lot of interest among researchers, the Arabic language, unfortunately, did not get an appropriate share of this interest. The Arabic language exhibits richness in semantics rarely found in other languages. To contribute to this field of work, we explore, in this paper, the aspect of combining evidences from multiple classifiers to improve accuracy of individual speech classification algorithms. The analysis covers fusion of evidence taken from different angles (perspectives) from statistical, to leaning, to evidence perspectives. Our experiments showed that ensemble-based classifiers achieve, on the average, an improvement in recognition accuracy of 4% or more, leading to overall recognition accuracies in the case of Arabic digits to more than 90%.
AB - Speech recognition continues to be a challenging research problem for diverse applications. The challenge is to develop better recognition systems, more robust, computationally more efficient, and versatile in nature. While western and eastern languages have attracted a lot of interest among researchers, the Arabic language, unfortunately, did not get an appropriate share of this interest. The Arabic language exhibits richness in semantics rarely found in other languages. To contribute to this field of work, we explore, in this paper, the aspect of combining evidences from multiple classifiers to improve accuracy of individual speech classification algorithms. The analysis covers fusion of evidence taken from different angles (perspectives) from statistical, to leaning, to evidence perspectives. Our experiments showed that ensemble-based classifiers achieve, on the average, an improvement in recognition accuracy of 4% or more, leading to overall recognition accuracies in the case of Arabic digits to more than 90%.
KW - Arabic speech recognition
KW - ensemble methods
KW - individual classifiers
KW - neural network
UR - https://www.scopus.com/pages/publications/85050884116
U2 - 10.1109/ICETAS.2017.8277845
DO - 10.1109/ICETAS.2017.8277845
M3 - Conference contribution
AN - SCOPUS:85050884116
T3 - 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
SP - 1
EP - 5
BT - 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
Y2 - 29 November 2017 through 1 December 2017
ER -