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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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REFINING AUTOMATIC SPEECH RECOGNITION SYSTEM FOR OLDER ADULTS.

Liu Chen1, Meysam Asgari1

  • 1Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon, USA.

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|June 23, 2023
PubMed
Summary
This summary is machine-generated.

Developing automatic speech recognition (ASR) for seniors is challenging. Transfer learning and attention mechanisms significantly improve ASR performance for older adults, even with limited data.

Keywords:
attention mechanismautomatic speech recognitionsenior populationsmall training datatransfer learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Speech Technology

Background:

  • High-quality automatic speech recognition (ASR) systems are difficult to build with limited data, especially for specific populations like seniors.
  • Existing open-source ASR systems trained on adult speech perform poorly on seniors due to acoustic differences.

Purpose of the Study:

  • To develop an ASR system for socially isolated seniors (80+ years old) with potential cognitive impairments using limited training data.
  • To investigate the effectiveness of transfer learning (TL) and attention mechanisms in improving ASR performance for this demographic.

Main Methods:

  • Utilized 12 hours of training data to develop an ASR system tailored for seniors.
  • Applied transfer learning (TL) by fine-tuning model parameters from a pre-trained adult ASR model.
  • Integrated an attention mechanism to leverage intermediate model information for enhanced performance.

Main Results:

  • ASR systems trained on adult data showed poor performance on the target senior population.
  • Transfer learning (TL) demonstrated a significant boost in ASR system performance for seniors.
  • The proposed approach, combining TL with an attention mechanism, achieved an additional 1.58% absolute performance improvement over the standard TL model.

Conclusions:

  • Transfer learning is effective in adapting ASR systems for senior speech.
  • Attention mechanisms further enhance ASR performance by utilizing model intermediate data.
  • The developed ASR system shows promise for improving communication accessibility for elderly individuals.