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Auditory Perception

Auditory Perception

The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the cochlea, a...

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Auditory Pathway

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Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
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Related Experiment Video

Updated: Jun 12, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Towards decoupling frontend enhancement and backend recognition in monaural robust ASR.

Yufeng Yang¹, Ashutosh Pandey¹, DeLiang Wang^1,2

¹Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, 43210, OH, United States.

Computer Speech & Language

|June 11, 2026

Summary

New speech enhancement models improve automatic speech recognition in noisy conditions. These systems decouple enhancement and recognition, outperforming models trained directly on noisy data for robust ASR.

Keywords:

CHiME-2 CHiME-4 Robust ASR Speech distortion Speech enhancement

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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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Published on: September 27, 2024

Related Experiment Videos

Last Updated: Jun 12, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Area of Science:

Speech processing
Machine learning
Signal processing

Background:

Speech enhancement (SE) algorithms can improve noisy speech intelligibility.
Monaural SE has historically underperformed compared to direct noisy speech training for automatic speech recognition (ASR).
A gap persists between SE advancements and robust ASR system development.

Purpose of the Study:

To bridge the gap between SE and ASR by proposing novel SE models.
To enable ASR systems trained solely on clean speech to perform effectively in noisy environments.
To advance robust ASR through improved SE frontends.

Main Methods:

Developed three SE models: Attentive Recurrent Network (ARN) in the time-domain, TF-CrossNet in the time-frequency domain, and MP-SENet based on magnitude-phase.
Decoupled SE frontend from the ASR backend, training the ASR only on clean speech.
Evaluated performance on WSJ, CHiME-2, LibriSpeech, and CHiME-4 corpora.

Main Results:

ARN, TF-CrossNet, and MP-SENet significantly improved ASR performance in noisy and reverberant conditions.
The proposed systems outperformed baseline ASR models trained directly on corrupted speech.
Achieved state-of-the-art results on CHiME-2 (5.6% WER) and CHiME-4 (3.3/4.4% WER), generalizing to real acoustic scenarios.

Conclusions:

The proposed SE models effectively eliminate the divide between SE and ASR.
Decoupled SE frontends allow for robust ASR systems trained on clean speech.
These advancements pave the way for more effective and generalizable robust ASR.