Speaker recognition with artificial neural networks and MEL-frequency cepstral coefficients correlations

The problem addressed in this paper is related to the fact that classical statistical approach for speaker recognition yields satisfactory results but at the expense of long length training and test utterances. An attempt to reduce the length of speaker samples is of great importance in the field of speaker recognition since the statistical approach, due to its limitations, is usually precluded from use in real-time applications. A novel method of text-independent speaker recognition which uses only the correlations among MFCCs, computed over selected speech segments of very-short length (approximately 120ms) is proposed. Three different neural networks — the Multi-Layer Perceptron (MLP), the Steinbuch's Learnmatrix (SLM) and the Self-Organizing Feature Finder (SOFF) — are evaluated in a speaker recognition task. The ability of dimensionality reduction of the SOFF paradigm is also discussed.