A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DISKEN G., TÜFEKCİ Z., SARIBULUT L., ÇEVİK U.

IETE TECHNICAL REVIEW, vol.34, no.3, pp.321-332, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 34 Issue: 3
  • Publication Date: 2017
  • Doi Number: 10.1080/02564602.2016.1185976
  • Journal Name: IETE TECHNICAL REVIEW
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.321-332
  • Keywords: Feature extraction, Identification, Speaker recognition, Verification, LINEAR PREDICTION, ROBUST SPEECH, WAVELET TRANSFORM, WORD RECOGNITION, MULTITAPER MFCC, ADDITIVE NOISE, VERIFICATION, IDENTIFICATION, COMPENSATION, COMBINATION
  • Çukurova University Affiliated: Yes

Abstract

Speech is a signal that includes speaker's emotion, characteristic specification, phoneme-information etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.