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Challenges and Methods in Annotating Natural Speech for Neurolinguistic Research.

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Area of Science:

  • Neuroscience
  • Linguistics
  • Computational Linguistics

Background:

  • Spoken language is fundamental to human communication, impacting cognition and social interaction.
  • Understanding natural language processing in the brain is crucial for neurobiology of language research.
  • Studying neural processes requires precise, time-locked speech data, which is challenging to obtain.

Purpose of the Study:

  • To address the need for detailed, time-locked descriptions of spontaneous speech.
  • To present novel semi-automatic pipelines for time-resolved annotation of natural language.
  • To facilitate the analysis of neural representations of linguistic features in ongoing speech.

Main Methods:

  • Development of two semi-automatic pipelines for time-resolved speech annotation.
  • Pipelines designed for German and Hebrew, with adaptability for other languages.
  • Focus on creating true verbatim transcripts reflecting all uttered elements.

Main Results:

  • Demonstrated the challenges in obtaining time-resolved annotations of spontaneous speech.
  • Developed functional pipelines adaptable to different languages.
  • Generated outputs enabling detailed analysis of linguistic features in spoken language.

Conclusions:

  • Semi-automatic pipelines can aid in analyzing the neural processing of spontaneous speech.
  • Methodological advancements are needed in natural language processing for speech annotation.
  • Further research can enhance understanding of how the brain processes everyday language.