Speech Synthesis for Gender Classification

Authors

  • Kawthar AlDhlan

DOI:

https://doi.org/10.3991/ijes.v5i1.6690

Abstract


This paper presents a gender identification system to be used for call forwarding in health related communications. The system listens to the caller then using speech synthesis, image processing, and linear support vector machine SVM identifies either he or she is a male or a female. This solution is imperative in a conservative country such as the Kingdom of Saudi Arabia in order to forward the call to a male or female practitioner. The originality of the approach is that no transcription is used to learn SVM models. To identify the gender of the caller, the trained SVM model of the reference pieces are compared to transcripts of the audio frequency record and are using the Levenshtein distance. For the identification of gender, we obtain an accuracy rate of 94% on a test flow containing 449 pieces of speech clips.

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Published

2017-03-29

How to Cite

AlDhlan, K. (2017). Speech Synthesis for Gender Classification. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 5(1), pp. 67–76. https://doi.org/10.3991/ijes.v5i1.6690

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Section

Papers