Abstract
Text-Independent Speaker Identification consists in finding out the identity of an individual using his/her voice independently of the content of the speech signal, that is, regardless the words uttered by the speaker. This problem is harder than Text-dependent speaker recognition where the speaker has to utter some specific word or phrase so he/she can be recognized. However, Text-Independent Speaker Identification is what we have to solve when the speaker has to be recognized without his/her collaboration as is frequently the case in many practical situations. Our proposal consists in searching within the speech signal for voiced speech content, which is the kind of speech produced when the vocal cords are vibrating. Once these segments of speech are identified, the formants are determined, formants are the resonance frequencies of the vocal tract. We use these formants to produce images which we believe should be different from one speaker to another, the way such images are built is original. Each image represent a specific speaker and so the problem of identifying speakers is turned into a problem of image recognition and we know how useful convolutional neural networks are for that purpose. For our experiments we used a collection of recordings from 21 individuals and achieved an accuracy of 92% outperforming the best results for text-independent identification published in recent works that used the same collection for testing.
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Camarena-Ibarrola, A., Reynoso, M., Figueroa, K. (2021). Text-Independent Speaker Identification Using Formants and Convolutional Neural Networks. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_9
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