The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices

2021/12/15 by Hindawi, Noor Ahmad Al, Shahin, Ismail, Nassif, Ali Bou
#Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2112.07940

Abstract

Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors. One of the most important components of SI is feature extraction, which has a substantial impact on the SI process and performance. As a result, numerous feature extraction strategies are thoroughly investigated, contrasted, and analyzed. This article exploits five distinct feature extraction methods for speaker identification in disguised voices under emotional environments. To evaluate this work significantly, three effects are used: high-pitched, low-pitched, and Electronic Voice Conversion (EVC). Experimental results reported that the concatenated Mel-Frequency Cepstral Coefficients (MFCCs), MFCCs-delta, and MFCCs-delta-delta is the best feature extraction method.

Related

• Vocal Style Factorization for Effective Speaker Recognition in Affective Scenarios
• Speaker Identification from emotional and noisy speech data using learned voice segregation and Speech VGG
• Speaker Emotion Recognition: Leveraging Self-Supervised Models for Feature Extraction Using Wav2Vec2 and HuBERT
• An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning
• Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features