Abstract
The human speech contains paralinguistic information used in many speech recognition applications like automatic speech recognition, speaker recognition, and verification. Gender from voice is considered as one of the essential tasks to be detected for such applications. To build a model from a training set, a set of relevant speech features is extracted in order to distinguish gender (i.e., female or male) from a speech signal. This paper focuses on comparison of the proposed neural network (NN) model with the different features like MFCC and mel spectrogram extracted from the speech signal to recognize the gender. Experiments are carried on Mozilla voice dataset and evaluated performance of the network. Experiments show that the combination of MFCC and mel feature sets shows the better accuracy with 94.32%.
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Chachadi, K., Nirmala, S.R. (2022). Voice-Based Gender Recognition Using Neural Network. In: Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-16-0739-4_70
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DOI: https://doi.org/10.1007/978-981-16-0739-4_70
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