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Related Experiment Video

Updated: Mar 31, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Discrimination Between Native and Non-Native Speech Using Visual Features Only.

Christos Georgakis, Stavros Petridis, Maja Pantic

    IEEE Transactions on Cybernetics
    |October 30, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study shows that visual cues from speech can identify non-native English speakers, even without audio. Appearance features, using hidden Markov models, achieved 76.5% accuracy in distinguishing accents.

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    Area of Science:

    • Biometrics
    • Speech Processing
    • Computer Vision

    Background:

    • Accent classification traditionally relies on audio features.
    • Visual speech analysis offers a complementary or alternative approach.
    • Temporal visual speech dynamics can reveal accent characteristics.

    Purpose of the Study:

    • To investigate the effectiveness of visual speech dynamics for accent identification without audio.
    • To develop an automated system for discriminating native from non-native English speech using only visual cues.
    • To evaluate the performance of different visual features for this task.

    Main Methods:

    • Developed a fully automated approach using exclusively visual speech information.
    • Systematically evaluated appearance and shape features.
    • Employed fusion of five hidden Markov models trained on appearance features.
    • Conducted subject-independent cross-validation on the MOBIO database.

    Main Results:

    • Appearance features consistently outperformed shape features.
    • Achieved a high performance of 76.5% accuracy on a text-dependent protocol.
    • The framework demonstrated efficiency on unseen speech examples, though with reduced accuracy.

    Conclusions:

    • Temporal visual speech dynamics are valuable for accent classification, especially in audio-degraded conditions.
    • Appearance-based features combined with hidden Markov models provide a robust method for accent identification.
    • Visual-only accent recognition shows promise for applications where audio is unavailable or unreliable.