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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Under-resourced dialect identification in Ao using source information.

Moakala Tzudir1, Shikha Baghel1, Priyankoo Sarmah1

  • 1Indian Institute of Technology Guwahati, Guwahati-781039, India.

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Summary

This study enhances automatic dialect identification for the Ao language by using novel speech features and data augmentation. Results show a significant improvement in identifying dialects, especially with optimized utterance duration.

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Area of Science:

  • Speech Processing
  • Computational Linguistics
  • Phonetics

Background:

  • Automatic Dialect Identification (DID) is crucial for understanding linguistic diversity.
  • The Ao language, a tonal language, presents unique challenges for DID due to its under-resourced nature.
  • Existing DID systems often struggle with tonal languages and limited data.

Purpose of the Study:

  • To develop an effective automatic dialect identification system for the Ao language.
  • To explore the utility of source features, specifically the gammatonegram of the linear prediction residual, for DID in tonal languages.
  • To investigate the impact of data augmentation and utterance duration on DID performance.

Main Methods:

  • Utilized the gammatonegram of the linear prediction residual as a novel feature for DID.
  • Applied data augmentation techniques to expand the limited Ao speech corpus.
  • Evaluated a baseline system and progressively incorporated source features (Silpr, Slms, S) and Mel frequency cepstral coefficients (MFCCs).
  • Conducted perception tests to determine optimal utterance duration for DID.

Main Results:

  • Data augmentation improved DID performance by 14%.
  • A baseline system with Slms achieved an F1-score of 53.84% for 3s utterances.
  • Incorporating source features Silpr and S boosted the F1-score to 60.69%.
  • The final system combining Silpr, S, Slms, and MFCCs reached an F1-score of 61.46%.

Conclusions:

  • The proposed gammatonegram feature is effective for DID in the tonal Ao language.
  • Data augmentation and the strategic inclusion of source features significantly enhance DID accuracy.
  • Optimizing utterance duration, informed by human perception, is vital for robust automatic dialect identification.