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Effect of sleepiness in the voice on speaker recognition performance

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EN
Abstrakty
EN
The issue of the influence of speaker state on voice recognition has been analysed mainly in relation to forensics and biometric security systems. Sleepiness in the voice is a rather under-researched problem, and the few works in this area focus almost exclusively on the recognition of sleepiness rather than on its influence on the change of the speaker's voice characteristics. This paper discusses the issue of the influence of the speaker's state on voice recognition, describes the acquisition method of the acoustic database of voice drowsiness recordings used in the tests. It also discusses the subjective sleepiness scales used in the study and presents the results of the influence of sleepiness on the effectiveness of automatic speaker recognition based on a classical system using the Mel-Frequency Cepstral Coefficients parameterisation and the Gaussian Mixture Models classification.
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art. no. 2021214
Opis fizyczny
Bibliogr. 12 poz., wykr.
Twórcy
  • Wroclaw University of Science and Technology, Department of Acoustics, Multimedia and Signal Processing, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
Bibliografia
  • 1. C. Zhang, T. Tan. Voice disguise and automatic speaker recognition. Forensic Science 35 International, 175:118-122, 2008.
  • 2. P. Staroniewicz. Effect of deliberate and non-deliberate natural voice disguise on speaker recognition performance. Acoustics, Acoustoelectronics and Electrical Engineering, 312-325, 2021.
  • 3. M. Farrus. Voice Disguise in Automatic Speaker Recognition. ACM Computing Surveys, 51(4), 2018.
  • 4. P. Staroniewicz. Influence of Natural Voice Disguise Techniques on Automatic Speaker Recognition. Proc. Of Joint Conference - Acoustics, IEEE, 2018.
  • 5. P. Staroniewicz. Considering basic emotional state information in speaker verification. Proc. 4th International Conference on Biometrics and Forensics (IWBF) IEEE, 2016.
  • 6. J. Krajewski, A. Batliner, M. Golz. Acoustic sleepiness detection: Framework and validation of a speech-adapted pattern approach. Behavior Research Methods, 41(3):795-804, 2009.
  • 7. J. Krajewski, S. Schnieder, D. Sommer, A. Batliner, B. Schuller. Applying Multiple Classifiers and Non-Linear Dynamic Features for Detecting Sleepiness from Speech. Neurocomputing, 84:65-75, 2012.
  • 8. A. Shahid et al. (eds.). STOP, THAT and One Hundred Other Sleep Scales. Springer Science+Business Media, 2012.
  • 9. A. A. Miley, G. Kecklund, T. Akerstedt. Comparing two versions of the Karolinska Sleepiness Scale (KSS). Sleep Biol. Rhythms, 14:257-260, 2016.
  • 10. D. A. Reynolds, R. C. Rose. Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models. IEEE Trans. Speech and Audio Proc., 3(1):72-83. 1995.
  • 11. D. A. Reynolds, T. F. Quatieri, R. B. Dunn. Speaker Verification Using Adapted Gausian Mixture Models. Digital Signal Processing, 10:19-41, 2000.
  • 12. A. Martin, A. Doddington, T. Kamm, M. Ordowski, M. Przybocki. The DET Curve in Assessment of Detection Task Performance, EuroSpeech 1997, Proceedings, 4:1895-1898, 1997.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-50c23892-6a11-46b5-bc39-9630210aba35
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