A robust accent classification system based on variational mode decomposition

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Darshana Subhash , Jyothish Lal G. , Premjith B. , Vinayakumar Ravi
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引用次数: 0

Abstract

State-of-the-art automatic speech recognition models often struggle to capture nuanced features inherent in accented speech, leading to sub-optimal performance in speaker recognition based on regional accents. Despite substantial progress in the field of automatic speech recognition, ensuring robustness to accents and generalization across dialects remains a persistent challenge, particularly in real-time settings. In response, this study introduces a novel approach leveraging Variational Mode Decomposition (VMD) to enhance accented speech signals, aiming to mitigate noise interference and improve generalization on unseen accented speech datasets. Our method employs decomposed modes of the VMD algorithm for signal reconstruction, followed by feature extraction using Mel-Frequency Cepstral Coefficients (MFCC). These features are subsequently classified using machine learning models such as 1D Convolutional Neural Network (1D-CNN), Support Vector Machine (SVM), Random Forest, and Decision Trees, as well as a deep learning model based on a 2D Convolutional Neural Network (2D-CNN). Experimental results demonstrate superior performance, with the SVM classifier achieving an accuracy of approximately 87.5% on a standard dataset and 99.3% on the AccentBase dataset. The 2D-CNN model further improves the results in multi-class accent classification tasks. This research contributes to advancing automatic speech recognition robustness and accent-inclusive speaker recognition, addressing critical challenges in real-world applications.
基于变模分解的鲁棒口音分类系统
最先进的自动语音识别模型往往难以捕捉重音语音中固有的细微特征,从而导致基于地区口音的说话人识别效果不尽如人意。尽管自动语音识别领域取得了长足进步,但确保对口音的鲁棒性和跨方言泛化仍是一项长期挑战,尤其是在实时环境中。为此,本研究引入了一种利用变异模式分解(VMD)来增强重音语音信号的新方法,旨在减轻噪声干扰,提高对未见重音语音数据集的泛化能力。我们的方法采用 VMD 算法的分解模式进行信号重建,然后使用梅尔-频率倒频谱系数(MFCC)进行特征提取。随后使用机器学习模型对这些特征进行分类,如一维卷积神经网络(1D-CNN)、支持向量机(SVM)、随机森林和决策树,以及基于二维卷积神经网络(2D-CNN)的深度学习模型。实验结果表明,SVM 分类器在标准数据集上的准确率约为 87.5%,在 AccentBase 数据集上的准确率为 99.3%,表现出色。2D-CNN 模型进一步提高了多类口音分类任务的结果。这项研究有助于提高自动语音识别的鲁棒性和口音包容性,解决实际应用中的关键挑战。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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