Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Auditory Feature Extraction Approach for Robust Pathological Voice Recognition.

Journal of voice : official journal of the Voice Foundation·2026

Same journal

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors.

SpringerPlus·2017

Same journal

Erratum to: Implication of Paris Agreement in the context of long-term climate mitigation goals.

SpringerPlus·2017

Same journal

Erratum to: Associations between adherence, depressive symptoms and health-related quality of life in young adults with cystic fibrosis.

SpringerPlus·2017

Same journal

Erratum to: Numerical method to compute acoustic scattering effect of a moving source.

SpringerPlus·2017

Same journal

Identifying appropriate protected areas for endangered fern species under climate change.

SpringerPlus·2017

Same journal

An Algorithm to detect balancing of iterated line sigraph.

SpringerPlus·2017

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 19, 2026

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

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

Published on: August 9, 2024

A bio-inspired feature extraction for robust speech recognition.

Youssef Zouhir¹, Kaïs Ouni¹

¹Research Unit: Signals and Mechatronic Systems, SMS, Higher School of Technology and Computer Science (ESTI), University of Carthage, Carthage, Tunisia.

|December 9, 2014

Summary

This summary is machine-generated.

This study introduces a novel feature extraction method for robust speech recognition in noisy conditions. The biologically inspired approach demonstrates superior performance over traditional techniques, enhancing accuracy in real-world scenarios.

Keywords:

Auditory filter model Feature extraction Hidden Markov Models Noisy speech recognition

More Related Videos

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

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Apr 19, 2026

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

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

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

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

Signal Processing
Auditory Modeling
Speech Recognition

Background:

Speech recognition systems struggle with performance degradation in noisy environments.
Traditional feature extraction methods like MFCC, LPCC, LPC, and PLP have limitations in handling diverse noise conditions.
Biologically inspired auditory models offer potential for more robust feature extraction.

Purpose of the Study:

To propose a novel feature extraction method for robust speech recognition in noisy environments.
To leverage a biologically inspired auditory model for improved speech feature representation.
To evaluate the proposed method's effectiveness against established techniques.

Main Methods:

A feature extraction method simulating outer/middle ear filtering using a low-pass filter.
Utilizing the Gammachirp auditory filterbank (GcFB) to model cochlear spectral behavior.
Employing Hidden Markov Models with continuous Gaussian Mixture densities (HMM-GM) for speech recognition.

Main Results:

The proposed method achieved higher speech recognition rates compared to PLP, LPC, LPCC, and MFCC.
Demonstrated improved robustness on speech signals corrupted by real-world noises.
The Gammachirp auditory filterbank (GcFB) based features showed significant advantages.

Conclusions:

The biologically inspired feature extraction method offers superior performance for robust speech recognition in noisy environments.
The proposed method outperforms classic techniques, providing a more effective approach for real-world applications.
Auditory modeling principles are crucial for developing advanced speech recognition systems.