Speaker Identification From Youtube Obtained Data

TL;DR

This paper presents an efficient speaker identification algorithm using Gaussian mixture models and Expectation Maximization, achieving up to 92.6% accuracy on YouTube data for long audio recordings.

Contribution

It introduces a robust speaker identification method employing GMM and EM for long, real-world YouTube datasets, improving accuracy over traditional techniques.

Findings

Achieved 79-82% identification rate with Vector Quantization.

Achieved 85-92.6% identification rate with GMM and EM.

Demonstrated effectiveness on long, diverse YouTube audio recordings.

Abstract

An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker identification is to identify the number of different speakers and prepare a model for that speaker by extraction, characterization and speaker-specific information contained in the speech signal. It has many diverse application specially in the field of Surveillance, Immigrations at Airport, cyber security, transcription in multi-source of similar sound source, where it is difficult to assign transcription arbitrary. The most commonly speech parametrization used in speaker verification, K-mean, cepstral analysis, is detailed. Gaussian mixture modeling, which is the speaker modeling technique is then explained. Gaussian mixture models (GMM), perhaps the most robust machine learning algorithm has been introduced examine and judge carefully speaker identification in text independent. The application or employment of Gaussian mixture models for monitoring & Analysing speaker identity is encouraged by the familiarity, awareness, or understanding gained through experience that Gaussian spectrum depict the characteristics of speaker's spectral conformational pattern and remarkable ability of GMM to construct capricious densities after that we illustrate 'Expectation maximization' an iterative algorithm which takes some arbitrary value in initial estimation and carry on the iterative process until the convergence of value is observed,so by doing various number of experiments we are able to obtain 79 ~ 82% of identification rate using Vector quantization and 85 ~ 92.6% of identification rate using GMM modeling by Expectation maximization parameter estimation depending on variation of parameter.