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MKELM based multi-classification model for foreign accent identification.

Kaleem Kashif¹, Abeer Alwan², Yizhi Wu³

¹Department of Information Engineering, Electronics and Telecommunication, Sapienza University Rome, Rome, 00184, Italy.

|September 12, 2024

Summary

This summary is machine-generated.

This study introduces a new Multi-Kernel Extreme Learning Machine (MKELM) model for foreign accent identification (FAID). The novel weighted MKELM model significantly improves accuracy in classifying multiple non-native English accents.

Keywords:

Foreign accent identification (FAID)Multi-kernel extreme learning machine (MKELM)Weighted classification scheme (WCS)

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Area of Science:

Speech processing
Machine learning
Computational linguistics

Background:

Automatic identification of foreign accents is vital for diverse speech technologies like speaker identification and enhancing Automatic Speech Recognition (ASR).
Current multi-class foreign accent identification (FAID) models struggle with performance, computational complexity, and feature selection, leading to low accuracy.
Non-native accents present unique challenges due to distinct phonetic, prosodic, and vocal characteristics.

Purpose of the Study:

To propose a novel framework for multi-class foreign accent identification (FAID) using the Multi-Kernel Extreme Learning Machine (MKELM) model.
To address the limitations of existing multi-classification models in handling multi-dimensional, unbalanced datasets and feature selection bottlenecks.
To enhance the accuracy and efficiency of identifying various non-native English accents.

Main Methods:

A Multi-Kernel Extreme Learning Machine (MKELM) model was developed for multi-class FAID.
Mel-frequency cepstral coefficients (MFCCs) and prosodic features were combined as input.
A novel weighted scheme was implemented, training pairwise binary classifiers independently and then applying weights for final classification.

Main Results:

The proposed MKELM model achieved an accuracy rate of 84.72% using a paired weighting scheme for FAID.
Traditional non-weighted multi-classification schemes resulted in a lower accuracy rate of 66.5%.
The MKELM model demonstrated superior accuracy, reduced computational complexity, and enhanced stability compared to existing methods.

Conclusions:

The proposed MKELM framework offers a significant advancement in multi-class foreign accent identification.
The novel weighted scheme effectively improves classification accuracy and model efficiency.
This approach provides a more robust and accurate solution for FAID systems across various applications.