Estimation of age from speech using excitation source features

Extraction of various distinct features from speech, Gaussian mixture models (GMMs) have been employed as classifiers. Age estimation performance has been inferred by employing excitation source features (LPCCs). The detected age of a speaker is deemed to belong to any of the 9 age groups of 5–10, 1...

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Bibliographic Details
Main Authors: Avikal, Shwetank, Sharma, Kritika, Barthwal, Anuragh, Nithin Kumar, K.C., Kumar Badhotiya, Gaurav
Format: Conference Proceeding
Language:English
Subjects:
Age approximation
Excitation source features
Linear prediction cepstral coefficients
Text independent age estimation
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Summary:Extraction of various distinct features from speech, Gaussian mixture models (GMMs) have been employed as classifiers. Age estimation performance has been inferred by employing excitation source features (LPCCs). The detected age of a speaker is deemed to belong to any of the 9 age groups of 5–10, 10–15, 15–20, 20–25, 25–30, 30–35, 35–40, 40–45 and 45–50. An age group speech corpus has been collected in this work by recording voice of speakers of Hindi language of different age groups and dialects using ten text prompts in neutral speech. Textually neutral Hindi words have been used to construct text prompts which have been recorded in neutral emotion. Different age group has been characterized by using these texts prompt. The average age performance of multispeaker (male + female) is around 94%. In this research, classification of different group of speakers has been done on the basis of excitation source in human speech.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2021.02.159