Analysis of sound for emotion speech recognition
Recognising emotion from the speech or Emotion Speech Recognition has been relatively recent research field in the speech recognition. This is useful for applications which require natural interaction between human and machine, for example ticket reservation machine, call centre application, as well...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10356/62647 |
| _version_ | 1811691445865676800 |
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| author | Nirmala Sari Karlina Halim |
| author2 | Lee Bu Sung, Francis |
| author_facet | Lee Bu Sung, Francis Nirmala Sari Karlina Halim |
| author_sort | Nirmala Sari Karlina Halim |
| collection | NTU |
| description | Recognising emotion from the speech or Emotion Speech Recognition has been relatively recent research field in the speech recognition. This is useful for applications which require natural interaction between human and machine, for example ticket reservation machine, call centre application, as well as in medical field. However, getting the reliable model for Emotion Speech Recognition is a challenge. In this report, four basics emotion (e.g. Happy, Angry, Anxious, and Sad) will be used to analyse the best features and classification model for Emotion Speech Recognition. Different approaches are explored and analysed to determine which approach gives the highest accuracy, using WEKA as the classification tool and Praat to extract the speech features. After experimenting with Praat, the best approach is implemented into real-time mobile application with Android platform. TarsosDSP is used as the external library to process the audio signal, as well as extract the speech features needed. |
| first_indexed | 2024-10-01T06:20:01Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/62647 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:20:01Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/626472023-03-03T20:30:08Z Analysis of sound for emotion speech recognition Nirmala Sari Karlina Halim Lee Bu Sung, Francis School of Computer Engineering Emerging Research Lab DRNTU::Engineering::Computer science and engineering Recognising emotion from the speech or Emotion Speech Recognition has been relatively recent research field in the speech recognition. This is useful for applications which require natural interaction between human and machine, for example ticket reservation machine, call centre application, as well as in medical field. However, getting the reliable model for Emotion Speech Recognition is a challenge. In this report, four basics emotion (e.g. Happy, Angry, Anxious, and Sad) will be used to analyse the best features and classification model for Emotion Speech Recognition. Different approaches are explored and analysed to determine which approach gives the highest accuracy, using WEKA as the classification tool and Praat to extract the speech features. After experimenting with Praat, the best approach is implemented into real-time mobile application with Android platform. TarsosDSP is used as the external library to process the audio signal, as well as extract the speech features needed. Bachelor of Engineering (Computer Science) 2015-04-24T06:22:16Z 2015-04-24T06:22:16Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62647 en Nanyang Technological University 45 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Nirmala Sari Karlina Halim Analysis of sound for emotion speech recognition |
| title | Analysis of sound for emotion speech recognition |
| title_full | Analysis of sound for emotion speech recognition |
| title_fullStr | Analysis of sound for emotion speech recognition |
| title_full_unstemmed | Analysis of sound for emotion speech recognition |
| title_short | Analysis of sound for emotion speech recognition |
| title_sort | analysis of sound for emotion speech recognition |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/62647 |
| work_keys_str_mv | AT nirmalasarikarlinahalim analysisofsoundforemotionspeechrecognition |