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Speech and Non-Speech Identification and Classification using KNN Algorithm

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Title: Speech and Non-Speech Identification and Classification using KNN Algorithm
Authors: Priya, T. Lakshmi rmtlpriya1208@gmail.com, Raajan, N.R.1, Raju, N.1, Preethi, P.1, Mathini, S.1
Source: Procedia Engineering. Sep2012, Vol. 38, p952-958. 7p.
Subjects: Speech perception, Nearest neighbor analysis (Statistics), Signal processing, Algorithms, Performance evaluation, Signal-to-noise ratio
Abstract: Abstract: Speech and non-speech identification along with its classification method that need to be improved in the endpoint detection for speech in noisy environments. The proposed method uses few features to increase the robustness in various noisy environments, and the classification used here KNN technique is applied to effectively combine these multiple features for classification of each speech signal. We evaluate the performance of the proposed method by conducting speech and non-speech classification experiments on noisy speech. We also investigate the importance of various features on speech and non-speech classification in noisy environments and by using this KNN algorithm to obtaining 80% accuracy. [Copyright &y& Elsevier]
Copyright of Procedia Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Speech and Non-Speech Identification and Classification using KNN Algorithm
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  Data: <searchLink fieldCode="AR" term="%22Priya%2C+T%2E+Lakshmi%22">Priya, T. Lakshmi</searchLink><i> rmtlpriya1208@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Raajan%2C+N%2ER%2E%22">Raajan, N.R.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Raju%2C+N%2E%22">Raju, N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Preethi%2C+P%2E%22">Preethi, P.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mathini%2C+S%2E%22">Mathini, S.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Procedia+Engineering%22">Procedia Engineering</searchLink>. Sep2012, Vol. 38, p952-958. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Speech+perception%22">Speech perception</searchLink><br /><searchLink fieldCode="DE" term="%22Nearest+neighbor+analysis+%28Statistics%29%22">Nearest neighbor analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink>
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  Label: Abstract
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  Data: Abstract: Speech and non-speech identification along with its classification method that need to be improved in the endpoint detection for speech in noisy environments. The proposed method uses few features to increase the robustness in various noisy environments, and the classification used here KNN technique is applied to effectively combine these multiple features for classification of each speech signal. We evaluate the performance of the proposed method by conducting speech and non-speech classification experiments on noisy speech. We also investigate the importance of various features on speech and non-speech classification in noisy environments and by using this KNN algorithm to obtaining 80% accuracy. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Procedia Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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