Abstract
For humans, speech is one of the vital communication channel used for interchanging information, knowledge, and thoughts. Identifying the age of a person based on his/her speech is an essential part of speech therapy and many telecommunication applications. Many speech-related disorders can be diagnosed and cured using age identification at early ages. Depending on the age group, particular speech therapy can be given to a child. In this research, typical speech sentences were used to identify the age of 200 Indian children from the age group of 4–8 years. Linear predictive cepstral coefficients (LPCC) (formant frequencies) was applied to extract 128 acoustic features using sustained phonation, reading and imitation tasks. Artificial neural network (ANN) and support vector machine (SVM) were used to build two classification models. Comparisons were made on classification accuracy. Classification results were substantially higher between the age group of 4 and 8 years. This work will further be extended to gender classification with more robust features and algorithms.
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Aggarwal, G., Singh, L. (2019). Age Classification with LPCC Features Using SVM and ANN. In: Fong, S., Akashe, S., Mahalle, P. (eds) Information and Communication Technology for Competitive Strategies. Lecture Notes in Networks and Systems, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-13-0586-3_40
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DOI: https://doi.org/10.1007/978-981-13-0586-3_40
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