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Cochlea-inspired speech recognition interface.

Mladen Russo1, Maja Stella2, Marjan Sikora2

  • 1Laboratory for Smart Environment Technologies, FESB - University of Split, Split, Croatia. mrusso@fesb.hr.

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|March 5, 2019
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Summary

New speech recognition models mimic the human ear's cochlea to significantly improve performance in noisy environments. These biophysical cochlear front-ends enhance automatic speech recognition (ASR) systems for real-world applications.

Keywords:
Biophysical cochlear modelNoise robustnessSpeech recognition interface

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Area of Science:

  • Auditory Neuroscience
  • Signal Processing
  • Artificial Intelligence

Background:

  • Automatic speech recognition (ASR) systems excel in quiet settings but falter in real-world noise.
  • Human auditory systems process sound effectively even in noisy environments, a capability lacking in current ASR.

Purpose of the Study:

  • To develop novel ASR front-end models that simulate the human auditory periphery.
  • To enhance ASR performance in realistic noisy conditions by incorporating biophysical cochlear models.

Main Methods:

  • Proposed two front-end models based on a biophysical cochlear model: one for signal reconstruction and another for direct coefficient construction.
  • Evaluated the signal reconstruction method for improved speech quality in noisy conditions and as a general noise reduction technique.
  • Utilized a continuous-density hidden Markov model (HMM) recognizer to compare the second front-end's performance against standard Mel-frequency cepstral coefficients (MFCC).

Main Results:

  • The signal reconstruction front-end demonstrated improved signal quality when applied to noisy speech.
  • The second front-end, constructing coefficients directly from basilar membrane response, showed significant performance improvements over MFCCs in various noisy conditions.
  • Experimental results confirmed the efficacy of the cochlear-based front-ends in enhancing ASR robustness.

Conclusions:

  • Biophysical cochlear models offer a promising approach to developing more robust ASR systems for real-world applications.
  • The proposed front-end models represent a significant advancement in simulating human auditory processing for improved machine interaction.
  • These findings pave the way for more natural and effective human-machine communication in diverse acoustic environments.