Arabic speaker identification system using multi features

Abstract EN

The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language.

In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages.

This paper aims to design an efficient SIS (text-independent) for the Arabic language.

The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase.

The second phase is testing, which searches the identification of the speaker.

In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency.

Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process.

The best classification technique is a K-nearest neighbors, where it gives higher precision of tremendous progress in applied technology for SIS, it is limited to English and some other languages.

This paper aims to design an efficient SIS (text-independent) for the Arabic language.

The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase.

The second phase is testing, which searches the identification of the speaker.

In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency.

Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process.

The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8% .