Pengenalan Bentuk Benda Berdasarkan Sinyal Suara dengan Transducer Mikrofon dan Teknologi Kinect
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Keywords
Abstract
Voice processing or speech recognition is growing rapidly hence it can be used for various applications such as moving a system or motion control and multimedia-based learning media. Implementation of speech recognition and image detection in this study using microphone transducer and kinect technology. This study aims to produce a system that can identify and recognize an object with word commands, such as circles, triangles, rectangles and many. In sound processing, sound feature extraction is carried out with Mel-Frequency Cepstrum Coeffecient (MFCC). Word modeling was done using statistical modeling, namely the Hidden Markov Model (HMM). HMM is able to provide an efficient mechanism for statistically modeling diversity in words or words. Data were collected with offline and online microphone transducers. This study matches the pattern of words through training and testing process. The output of this system is a recognizable word based on the highest probability and displaying the object shape based on the recognized word, namely circle, triangle and quadrilateral. Test results with mirofon tranducers, for 85% trained sources, 81.5% untrained sources, and 84% untrained Kinect source testing hence that word recognition systems can be implemented with Kinect technology.
Keywords : speech processing, HMM, MFCC, Kinect
Abstrak
Pengolahan suara atau pengenalan kata berkembang pesat sehingga dapat digunakan untuk berbagai aplikasi seperti menggerakan suatu sistem atau kontrol gerak dan media pembelajaran berbasis multimedia. Implementasi pengenalan suara dan deteksi citra pada penelitian ini menggunakan transducer mikrofon dan teknologi kinect. Penelitian ini bertujuan untuk menghasilkan sistem yang dapat mengidentifikasi dan mengenali suatu objek dengan perintah kata, seperti lingkaran, segitiga, segiempat dan segibanyak. Dalam pengolahan suara dilakukan ekstraksi ciri suara dengan Mel-Frequency Cepstrum Coeffecient (MFCC). Pemodelan kata dilakukan dengan menggunakan pemodelan statistik yaitu Hidden Markov Model (HMM). HMM mampu memberikan mekanisme yang efisien untuk memodelkan secara statistik keragaman dalam ucapan atau kata. Pengambilan data sampel dengan transducer mikrofon secara offline dan online. Pada penelitian ini pencocokan pola kata melalui proses pelatihan dan pengujian kata. Keluaran sistem ini berupa kata yang dikenali berdasarkan probabilitas tertinggi dan menampilkan bentuk benda berdasarkan kata yang dikenali. Prosesnya setelah kata dikenali, sistem akan mentracking citra benda berdasarkan bentuk benda kemudian menampilkan bentuk benda yaitu lingkaran, segitiga, segiempat dan segibanyak. Hasil pengujian dengan tranducer mirofon, untuk sumber terlatih 85%, sumber tidak terlatih 81,5%, dan pengujian dengan Kinect sumber tidak terlatih 84% sehingga sistem pengenalan kata dapat diimplementasikan dengan teknologi Kinect.
Kata Kunci : speech processing, HMM, MFCC, kinect
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