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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal

Byeongwook Lee¹, Kwang-Hyun Cho¹

¹Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Scientific Reports

|November 24, 2016

Summary

This summary is machine-generated.

This study introduces a novel speech segmentation method using the speech envelope's phase information. This approach enhances speech recognition accuracy and robustness, especially in noisy conditions.

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Last Updated: Mar 11, 2026

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Published on: August 9, 2024

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

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A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Area of Science:

Neuroscience
Signal Processing
Computational Linguistics

Background:

Conventional speech segmentation uses fixed frame sizes, failing to capture speech's quasi-regular structure and leading to recognition errors in noise.
Neurophysiological studies suggest auditory cortex neuronal oscillations and their phase guide speech segmentation across timescales.
The speech envelope's phase-locked relationship with neuronal oscillations indicates its role in multi-timescale speech segmentation.

Purpose of the Study:

To quantitatively investigate the speech envelope's instantaneous phase as a temporal reference for speech segmentation.
To evaluate a novel segmentation scheme based on speech envelope phase for improved information extraction and recognition performance.

Main Methods:

Developed a speech segmentation scheme utilizing the instantaneous phase information of the speech envelope.
Evaluated the scheme's effectiveness through information gain and speech recognition performance tests in various noisy environments.

Main Results:

The proposed segmentation scheme extracts more information from speech signals compared to conventional methods.
Demonstrated significantly improved robustness and recognition performance in noisy environments.

Conclusions:

The speech envelope's phase information serves as a valuable temporal reference for effective speech segmentation.
This phase-based approach offers a promising solution for enhancing automatic speech recognition, particularly under challenging acoustic conditions.