2022Unpublished venueRequires access

Factors responsible and phases of speaker recognition system

Hunny, Ayush Goyal

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Abstract

The method of identifying a speaker based on his or her speech is known as automatic speaker recognition. Speaker/voice recognition is a biometric sensory device that recognizes people by their voices. Most speaker recognition systems nowadays are focused on spectral information, which means they use spectral information derived from speech signal segments of 10-30 ms in length. However, if the received speech signal contains some noise, the cepstral-based system's output suffers. The primary goal of the study is to see the various factors responsible for improved performance of the speaker recognition systems by modeling prosodic features, and phases of speaker recognition system. Furthermore, in the presence of background noise, the analysis focused on a text-independent speaker recognition system.

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What this paper is about

The method of identifying a speaker based on his or her speech is known as automatic speaker recognition. Speaker/voice recognition is a biometric sensory device that recognizes people by their voices. Most speaker recognition systems nowadays are focused on spectral information, which means they use spectral information derived from speech signal segments of 10-30 ms in length. However, if the received speech signal contains some noise, the cepstral-based system's output suffers. The primary goal of the study is to see the various factors responsible for improved performance of the speaker recognition systems by modeling prosodic features, and phases of speaker recognition system. Furthermore, in the presence of background noise, the analysis focused on a text-independent speaker recognition system.

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Available abstract

The method of identifying a speaker based on his or her speech is known as automatic speaker recognition. Speaker/voice recognition is a biometric sensory device that recognizes people by their voices. Most speaker recognition systems nowadays are focused on spectral information, which means they use spectral information derived from speech signal segments of 10-30 ms in length. However, if the received speech signal contains some noise, the cepstral-based system's output suffers. The primary goal of the study is to see the various factors responsible for improved performance of the speaker recognition systems by modeling prosodic features, and phases of speaker recognition system. Furthermore, in the presence of background noise, the analysis focused on a text-independent speaker recognition system.

Key concepts: Speech recognition, Speaker recognition, Computer science, Speaker diarisation, Mel-frequency cepstrum, Biometrics, Noise (video), SIGNAL (programming language)

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