Automatic Emotion Recognition from Speech Using Artificial Neural Networks with Gender-Dependent Databases
Automatic Emotion Recognition (AER) from speech is one of the most important sub domains in affective computing. We have created and analyzed two emotional speech databases from male and female speech. Instead of using the phonetic and prosodic features we have used the Discrete Wavelet Transform (D...
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| creator | Firoz, S.A. Raji, S.A. Babu, A.P. |
| description | Automatic Emotion Recognition (AER) from speech is one of the most important sub domains in affective computing. We have created and analyzed two emotional speech databases from male and female speech. Instead of using the phonetic and prosodic features we have used the Discrete Wavelet Transform (DWT) technique for feature vector creation. Artificial neural network is used for pattern classification and recognition. We obtained a recognition accuracy of 72.055% in case of male speech database and 65.5% recognition in case of female speech database. Malayalam (one of the South Indian languages) was chosen for the experiment. We have recognized the four emotions neutral, happy, sad and anger by using Discrete Wavelet Transforms (DWT) and Artificial Neural Network (ANN) and the performance for the two databases are compared. |
| doi_str_mv | 10.1109/ACT.2009.49 |
| format | Conference Proceeding |
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We have created and analyzed two emotional speech databases from male and female speech. Instead of using the phonetic and prosodic features we have used the Discrete Wavelet Transform (DWT) technique for feature vector creation. Artificial neural network is used for pattern classification and recognition. We obtained a recognition accuracy of 72.055% in case of male speech database and 65.5% recognition in case of female speech database. Malayalam (one of the South Indian languages) was chosen for the experiment. We have recognized the four emotions neutral, happy, sad and anger by using Discrete Wavelet Transforms (DWT) and Artificial Neural Network (ANN) and the performance for the two databases are compared.</description><subject>Affective Computing</subject><subject>Artificial neural networks</subject><subject>Automatic Emotion Recognition</subject><subject>Discrete Wavelet Transform</subject><subject>Discrete wavelet transforms</subject><subject>Emotion recognition</subject><subject>Filter bank</subject><subject>Filtering</subject><subject>Frequency</subject><subject>Humans</subject><subject>Low pass filters</subject><subject>Multi Layer Perceptron</subject><subject>Speech</subject><isbn>1424453216</isbn><isbn>9781424453214</isbn><isbn>0769539157</isbn><isbn>9780769539157</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotzMtOAjEYQOEaY6IgK5du-gKDvXe6nAyIJkQTxTVpZ_5ClbmkLSG-vRFdnW91ELqjZE4pMQ9VvZkzQsxcmAs0IVoZyQ2V-hJNqGBCSM6oukazlIIjTGklpdY36LM65qGzOTR42Q05DD1-g2bY9eFsH4cOv48AzR5_pNDvcBVz8KEJ9oBf4BjPyachfiV8CnmPV9C3EIsFjL_oM17YbJ1NkG7RlbeHBLP_TtHmcbmpn4r16-q5rtZFMCQXQjQArSSeMuu8UqSk3DFvSg2iVYoJUzLpidde8VaXxjvHpGqFp440VBg-Rfd_2wAA2zGGzsbvreRa6ZLxHxQXV_4</recordid><startdate>200912</startdate><enddate>200912</enddate><creator>Firoz, S.A.</creator><creator>Raji, S.A.</creator><creator>Babu, A.P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200912</creationdate><title>Automatic Emotion Recognition from Speech Using Artificial Neural Networks with Gender-Dependent Databases</title><author>Firoz, S.A. ; Raji, S.A. ; Babu, A.P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-44ceed50f12abf660813b2f987e4d66249825f0f7f63d789fbb256d4f1b0c1493</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Affective Computing</topic><topic>Artificial neural networks</topic><topic>Automatic Emotion Recognition</topic><topic>Discrete Wavelet Transform</topic><topic>Discrete wavelet transforms</topic><topic>Emotion recognition</topic><topic>Filter bank</topic><topic>Filtering</topic><topic>Frequency</topic><topic>Humans</topic><topic>Low pass filters</topic><topic>Multi Layer Perceptron</topic><topic>Speech</topic><toplevel>online_resources</toplevel><creatorcontrib>Firoz, S.A.</creatorcontrib><creatorcontrib>Raji, S.A.</creatorcontrib><creatorcontrib>Babu, A.P.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Firoz, S.A.</au><au>Raji, S.A.</au><au>Babu, A.P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic Emotion Recognition from Speech Using Artificial Neural Networks with Gender-Dependent Databases</atitle><btitle>2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies</btitle><stitle>ACT</stitle><date>2009-12</date><risdate>2009</risdate><spage>162</spage><epage>164</epage><pages>162-164</pages><isbn>1424453216</isbn><isbn>9781424453214</isbn><eisbn>0769539157</eisbn><eisbn>9780769539157</eisbn><abstract>Automatic Emotion Recognition (AER) from speech is one of the most important sub domains in affective computing. We have created and analyzed two emotional speech databases from male and female speech. Instead of using the phonetic and prosodic features we have used the Discrete Wavelet Transform (DWT) technique for feature vector creation. Artificial neural network is used for pattern classification and recognition. We obtained a recognition accuracy of 72.055% in case of male speech database and 65.5% recognition in case of female speech database. Malayalam (one of the South Indian languages) was chosen for the experiment. We have recognized the four emotions neutral, happy, sad and anger by using Discrete Wavelet Transforms (DWT) and Artificial Neural Network (ANN) and the performance for the two databases are compared.</abstract><pub>IEEE</pub><doi>10.1109/ACT.2009.49</doi><tpages>3</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Affective Computing Artificial neural networks Automatic Emotion Recognition Discrete Wavelet Transform Discrete wavelet transforms Emotion recognition Filter bank Filtering Frequency Humans Low pass filters Multi Layer Perceptron Speech |
| title | Automatic Emotion Recognition from Speech Using Artificial Neural Networks with Gender-Dependent Databases |
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