Article · Bulletin of the National Engineering Academy of the Republic of Kazakhstan · October 15, 2020

VOICE IDENTIFICATION BASED ON THE I-VECTOR AND DEEP NEURAL NETWORKS USING SHORT UTTERANCES

Orken J. Mamyrbayev, A. Akhmediyarov, A. Kydyrbekov, Nurbapa Mekebayev, Mussa Turdalyuly

Open via DOI PDFDOI 10.47533/2020.1606-146x.09

Abstract

Text-independent voice recognition of the user using short sentences is a very difficult task due to the large spread and inconsistency of the content between short sentences, in order to improve user recognition by voice, it is planned to highlight several sets of distinguishing features that contain more information related to the voice. The results show that the i-vector DNN system is superior to the GMM i-vector system for various durations. However, the characteristics of both systems deteriorate significantly as the duration of the sentences decreases. To solve this problem, we propose two new nonlinear mapping methods that train DNN models to map i-vectors extracted from short sentences to their corresponding i-vectors of long sentences.

Speech Recognition and SynthesisSpeech and Audio ProcessingBiometric Identification and Securityvoice identificationtext-independent speaker recognitioni-vectors

VOICE IDENTIFICATION BASED ON THE I-VECTOR AND DEEP NEURAL NETWORKS USING SHORT UTTERANCES

Abstract

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