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Related Experiment Video

Updated: Jul 16, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Published on: September 27, 2024

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ACOUSTICALLY-DRIVEN PHONEME REMOVAL THAT PRESERVES VOCAL AFFECT CUES.

Camille Noufi1, Jonathan Berger1, Michael Frank2

  • 1Stanford University, Center for Computer Research in Music and Acoustics, Stanford, CA, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|September 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to isolate vocal affect cues by removing linguistic speech information. This technique effectively captures paralinguistic indicators for clinical applications, aiding in affect recognition.

Keywords:
affectelectroglottagraphyparalanguagephoneme removalspeechvoice transformation

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Area of Science:

  • Speech processing
  • Bioacoustics
  • Affective computing

Background:

  • Assessing vocal affect is crucial in clinical settings.
  • Language impairment in clinical populations confounds affect detection.
  • Existing methods struggle to separate linguistic and paralinguistic speech features.

Purpose of the Study:

  • To develop a method for removing linguistic information from speech.
  • To isolate paralinguistic indicators of affect for objective measurement.
  • To create a tool for clinical tests of vocal affect sensitivity unimpeded by language deficits.

Main Methods:

  • Simultaneous recording of speech audio and electroglottographic (EGG) signals.
  • Estimation of vocal tract filter response and amplitude envelope from audio.
  • Utilizing EGG signals for voice source activity independent of phonetic articulation.
  • Developing a transformed signal maximizing paralinguistic cues while minimizing phonetic information.

Main Results:

  • A novel signal was created to capture bioacoustic cues to affect.
  • The transformed signal successfully eliminated phonetic cues to verbal meaning.
  • Perception experiments demonstrated high similarity between original and transformed signals regarding perceived affect.
  • Online listeners showed consistent affect ratings for matched audio and EGG-derived signals.

Conclusions:

  • The proposed method effectively separates paralinguistic affect cues from linguistic content in speech.
  • This technique holds significant potential for clinical assessment of vocal affect recognition.
  • The findings validate the method's efficacy in preserving and highlighting affective vocal characteristics.