Vector Quantization based Speaker Identification
TL;DR: The main aim of this paper is speaker identification, which consists of comparing a speech signal from an unknown speaker to a database of known speakers and then measuring the difference.
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Abstract: The automatic speaker recognition technologies have developed into more and more important modern technologies required by many speech-aided applications. The main challenge for automatic speaker recognition is to deal with the variability of the environments and channels from where the speech was obtained. Speaker recognition system is a system which recognizes the speaker as opposed to what is being said by the speaker as in case of speech recognition. Speaker recognition technology makes it possible to the speaker's voice to control access to restricted services, for example, phone access to banking, database services, shopping or voice mail, and access to secure equipments. The main aim of this paper is speaker identification, which consists of comparing a speech signal from an unknown speaker to a database of known speakers. The methodology followed in this paper for Speaker identification is using Feature Extraction process and then Vector Quantization of extracted features is done using k-means algorithm. At last, the speaker is identified by comparing the data from a tested speaker to the database of each speaker and then measuring the difference.
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Citations
Speaker identification through artificial intelligence techniques: A comprehensive review and research challenges
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F.K. Soong,Aaron E. Rosenberg,Lawrence R. Rabiner,Biing-Hwang Juang +3 more
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TL;DR: A vector quantization (VQ) codebook was used as an efficient means of characterizing the short-time spectral features of a speaker and was used to recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) classification rule.
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