mlsanityData to 14 Oct 2026

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

arXiv 2112.07940 ↗First posted 15 Dec 2021cs.SD cs.CL eess.AScited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 38 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

5 of 38 references.

References · 5

  1. 2018Novel Cascaded Gaussian Mixture Model-Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environmentscited by 4
  2. 2021CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditionscited by 4
  3. 2014Tied Probabilistic Linear Discriminant Analysis for Speech Recognitioncited by 2
  4. 2017Studying and Enhancing Talking Condition Recognition in Stressful and Emotional Talking Environments Based on HMMs, CHMM2s and SPHMMscited by 2
  5. 2018Identifying Speakers Using Their Emotion Cuescited by 2

Built on by

Not cited yet.