Abstract
Language resources for Urdu language are not well developed. In this work, we summarize our work on the development of Urdu speech corpus for isolated words. The Corpus comprises of 250 isolated words of Urdu recorded by ten individuals. The speakers include both native and non-native, male and female individuals. The corpus can be used for both speech and speaker recognition tasks. We also report our results on automatic speech recognition task for the said corpus. The framework extracts Mel Frequency Cepstral Coefficients along with the velocity and acceleration coefficients, which are then fed to different classifiers to perform recognition task. The classifiers used are Support Vector Machines, Random Forest and Linear Discriminant Analysis. Experimental results show that the best results are provided by the Support Vector Machines with a test set accuracy of 73 %. The results reported in this work may provide a useful baseline for future research on automatic speech recognition of Urdu.
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Ali, H., Ahmad, N., Hafeez, A. (2016). Urdu Speech Corpus and Preliminary Results on Speech Recognition. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_24
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