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Related Experiment Video

Updated: Aug 9, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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ROLE SPECIFIC LATTICE RESCORING FOR SPEAKER ROLE RECOGNITION FROM SPEECH RECOGNITION OUTPUTS.

Nikolaos Flemotomos1, Panayiotis Georgiou1, David C Atkins2

  • 1Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|February 22, 2023
PubMed
Summary

This study introduces a new method for Speaker Role Recognition (SRR) by using specialized Automatic Speech Recognition (ASR) outputs. This approach preserves crucial linguistic information for more accurate role identification.

Keywords:
language modellattice rescoringspeaker role recognitionspeech recognition

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Area of Science:

  • Computational Linguistics
  • Speech Processing
  • Artificial Intelligence

Background:

  • Speaker Role Recognition (SRR) relies on identifying linguistic patterns in conversational speech.
  • Current SRR methods often process output from Automatic Speech Recognition (ASR) systems.
  • Potential information loss occurs with early pruning in ASR decoding.

Purpose of the Study:

  • To propose an alternative method for Speaker Role Recognition (SRR).
  • To leverage role-specific Automatic Speech Recognition (ASR) outputs for improved SRR.
  • To avoid information loss inherent in traditional ASR processing pipelines.

Main Methods:

  • Developed a technique using role-specific ASR outputs.
  • Rescored the lattice generated during the first pass of ASR decoding.
  • Avoided premature lattice pruning to retain all linguistic data.

Main Results:

  • The proposed method effectively reveals role-specific linguistic characteristics.
  • Utilizing rescoring of ASR lattices preserves information crucial for SRR.
  • This approach mitigates the risk of information loss compared to standard methods.

Conclusions:

  • The novel approach enhances the identification of linguistic patterns for Speaker Role Recognition.
  • Rescoring ASR lattices offers a more robust pathway for SRR tasks.
  • This method provides a valuable alternative for accurate speaker role identification in conversations.