Unified voice analysis: speaker recognition, age group and gender estimation using spectral features and machine learning classifiers

Kaya Akgün; Şerif Ali Sadık

Makale detayları

Unified voice analysis: speaker recognition, age group and gender estimation using spectral features and machine learning classifiers

Yazarlar:

Kaya Akgün | Şerif Ali Sadık

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Speech Recognition

Dergi:

Journal of Scientific Reports-A Issue 057, pp.12-26

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314806

pISSN:

2687-6167

Anahtar kelimeler:

Özet:

 Predicting speakers personal traits from voice data has been a subject of attention in many fields such as forensic cases, automatic voice response systems, and biomedical applications. Within the scope of this study, gender and age group prediction was made with the voice data recorded from 24 volunteers. Mel-frequency cepstral coefficients MFCC were extracted from the audio data as hybrid timefrequency domain features, and fundamental frequencies and formants were extracted as frequency domain features. These obtained features were fused in a feature pool and age group and gender estimation studies were carried out with 4 different machine learning algorithms. According to the results obtained, the age groups of the participants could be classified with 93 accuracy and the genders with 99 accuracy with the Support Vector Machines algorithm. Also, speaker recognition task was successfully completed with 93 accuracy with the Support Vector Machines.

 Predicting speakers personal traits from voice data has been a subject of attention in many fields such as forensic cases, automatic voice response systems, and biomedical applications. Within the scope of this study, gender and age group prediction was made with the voice data recorded from 24 volunteers. Mel-frequency cepstral coefficients MFCC were extracted from the audio data as hybrid timefrequency domain features, and fundamental frequencies and formants were extracted as frequency domain features. These obtained features were fused in a feature pool and age group and gender estimation studies were carried out with 4 different machine learning algorithms. According to the results obtained, the age groups of the participants could be classified with 93 accuracy and the genders with 99 accuracy with the Support Vector Machines algorithm. Also, speaker recognition task was successfully completed with 93 accuracy with the Support Vector Machines.

 Predicting speakers personal traits from voice data has been a subject of attention in many fields such as forensic cases, automatic voice response systems, and biomedical applications. Within the scope of this study, gender and age group prediction was made with the voice data recorded from 24 volunteers. Mel-frequency cepstral coefficients MFCC were extracted from the audio data as hybrid timefrequency domain features, and fundamental frequencies and formants were extracted as frequency domain features. These obtained features were fused in a feature pool and age group and gender estimation studies were carried out with 4 different machine learning algorithms. According to the results obtained, the age groups of the participants could be classified with 93 accuracy and the genders with 99 accuracy with the Support Vector Machines algorithm. Also, speaker recognition task was successfully completed with 93 accuracy with the Support Vector Machines.

 Predicting speakers personal traits from voice data has been a subject of attention in many fields such as forensic cases, automatic voice response systems, and biomedical applications. Within the scope of this study, gender and age group prediction was made with the voice data recorded from 24 volunteers. Mel-frequency cepstral coefficients MFCC were extracted from the audio data as hybrid timefrequency domain features, and fundamental frequencies and formants were extracted as frequency domain features. These obtained features were fused in a feature pool and age group and gender estimation studies were carried out with 4 different machine learning algorithms. According to the results obtained, the age groups of the participants could be classified with 93 accuracy and the genders with 99 accuracy with the Support Vector Machines algorithm. Also, speaker recognition task was successfully completed with 93 accuracy with the Support Vector Machines.

Makale detayları

Unified voice analysis: speaker recognition, age group and gender estimation using spectral features and machine learning classifiers

Yazarlar:

Kaya Akgün | Şerif Ali Sadık

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Speech Recognition

Dergi:

Journal of Scientific Reports-A Issue 057, pp.12-26

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314806

pISSN:

2687-6167

Gender estimation

Age group estimation

Speaker recognition

Support Vector Machine

k-nearest neighbors

gradient boosting

classification and regression tree

Sayı

Makale detayları

Yazarlar: Kaya Akgün | Şerif Ali Sadık

Yayıncı: Kütahya Dumlupinar University

Ana Başlıklar: Speech Recognition

Dergi: Journal of Scientific Reports-A Issue 057, pp.12-26

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001314806

pISSN: 2687-6167

Sayı

Yazarlar:

Kaya Akgün | Şerif Ali Sadık

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Speech Recognition

Dergi:

Journal of Scientific Reports-A Issue 057, pp.12-26

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314806

pISSN:

2687-6167