Robust FHPD Features from Speech Harmonic Analysis for Speaker Identification

Authors

Shuiping Wang, School of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing 210094, P. R. China \\ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China \\ School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. ChinaFollow
Zhenmin Tang
Ye Jiang
Ying Chen

Author Country (or Countries)

P. R. China

Abstract

Speaker identification accuracy decreases significantly in the presence of additive noise. In this paper, we propose a robust speech feature extraction method, which is based on the harmonic structure of voiced segments. The robust features are composed of fundamental and harmonic peak data from short-time spectrum. These features are evaluated by thirty speaker data from TIMIT database and additive noise signals from NOISEX-92 database with clean training and noisy testing samples. Results reflect that under low SNR (signal-to-noise ratio) environments new features achieve better performance than conventionalMFCC (Mel-Frequency Cepstral Coefficients) parameters.

Suggested Reviewers

N/A

Recommended Citation

Wang, Shuiping; Tang, Zhenmin; Jiang, Ye; and Chen, Ying (2013) "Robust FHPD Features from Speech Harmonic Analysis for Speaker Identification," Applied Mathematics & Information Sciences: Vol. 07: Iss. 4, Article 45.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss4/45