Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Voice Activity Detection (VAD) refers to the problem of distinguishing speech segments from background noise. Numerous approaches have been proposed for this purpose. Some are based on features derived from the por spectral density, others exploit the periodicity of the signal. The goal of this letter is to investigate the joint use of source and filter-based features. Interestingly, a mutual information-based assessment shows superior discrimination por for the source-related features, especially the proposed ones. The features are further the input of an artificial neural network-based classifier trained on a multi-condition database. Two strategies are proposed to merge source and filter information: feature and decision fusion. Our experiments indicate an absolute reduction of 3% of the equal error rate when using decision fusion. The final proposed system is compared to four state-of-the-art methods on 150 minutes of data recorded in real environments. Thanks to the robustness of its source-related features, its multi-condition training and its efficient information fusion, the proposed system yields over the best state-of-the-art VAD a substantial increase of accuracy across all conditions (24% absolute on average).
['Thomas Drugman', 'Yannis Stylianou', 'Yusuke Kida', 'Masami Akamine']
I am writing a research paper on a idea Voice Activity Detection: Merging Source and Filter-based Information
[{"from": "human", "value": "I am writing a research paper on a idea Voice Activity Detection: Merging Source and Filter-based Information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Voice Activity Detection (VAD) refers to the problem of distinguishing speech segments from background noise. Numerous approaches have been proposed for this purpose. Some are based on features derived from the por spectral density, others exploit the periodicity of the signal. The goal of this letter is to investigate the joint use of source and filter-based features. Interestingly, a mutual information-based assessment shows superior discrimination por for the source-related features, especially the proposed ones. The features are further the input of an artificial neural network-based classifier trained on a multi-condition database. Two strategies are proposed to merge source and filter information: feature and decision fusion. Our experiments indicate an absolute reduction of 3% of the equal error rate when using decision fusion. The final proposed system is compared to four state-of-the-art methods on 150\u00a0minutes of data recorded in real environments. Thanks to the robustness of its source-related features, its multi-condition training and its efficient information fusion, the proposed system yields over the best state-of-the-art VAD a substantial increase of accuracy across all conditions (24% absolute on average)."}]