Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network

• Main Authors: Fatchul Arifin, Tri Arief Sardjono, Mauridhy Hery Purnomo
• Format: Article
• Language: English
• Published: Institute for Research and Public Services 2010-08-01
• Series: IPTEK: The Journal for Technology and Science
• Subjects: Electrolarynx speech recognation; Pulse Coupled Neural Network (PCNN); Multi Layer Perceptron (MLP)
• Online Access: http://iptek.its.ac.id/index.php/jts/article/view/45/37
• ISSN: 0853-4098; 2088-2033

The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter) also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.