Abstract
Isolated word recognition serve as an important aspect of speech recognition problem. This paper contributes a solution of speaker-independent isolated word recognition based on different statistical analysis and feature selection method. In this work different parametric and nonparametric statistical algorithm such as analysis of variance (ANOVA) and Kruskal–Wallis are used to rank the features and incremental feature selection (IFS) to find the efficient features set. The objective of applying statistical analysis algorithm and feature selection technique on the cepstral feature is to improve the word recognition performance using efficient and optimal number of feature set. The experimental analysis is carried out using two machine learning techniques such as Artificial Neural Network (ANN) and Support vector machine (SVM) classifier. Performance of both the classifier has been evaluated and described in this paper. From the experimental analysis it has been observed that statistical analysis with feature selection technique provides better result for the two classifier as compared to original all cepstral features.
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Acknowledgements
The Authors gratefully acknowledge Dr. Pradip K. Das, Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati (IITG) and also acknowledge his students worked under his guidance for providing database support for this work. Dr. Pradip K. Das (http://www.iitg.ac.in/pkdas/), professor of IIT, Guwahati has the research interest of Digital Signal Processing, Speech Processing, Man-Machine Intelligence Systems and this work has been supported by him.
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Debnath, S., Roy, P. (2019). Isolated Word Recognition Based on Different Statistical Analysis and Feature Selection Technique. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_46
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DOI: https://doi.org/10.1007/978-981-13-0617-4_46
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