ROBOT LENGAN PENGAMBIL BENDA UNTUK MEMBANTU PASIEN DENGAN PERINTAH SUARA MENGGUNAKAN METODE MFCC DAN NEURAL NETWORK

Robot lengan pengambil benda dengan perintah suara adalah sebuah robot yang dapat digunakan untuk membantu manusia mengambil benda yang diinginkan dengan menggunkan perintah suara. Robot lengan ini diterapkan untuk membantu pasien yang memiliki keterbatasan gerak dalam mengambilkan benda yang diinginkan. Penggenalan perintah suara diproses menggunakan metode MFCC (Mel-Frequency Cepstrum Coefficients) dan ANN (Artificial Neural Network). Robot lengan juga dilengkapai kamera untuk mendeteksi benda yang akan diambil. Sensor ultrasonik diletakan pada ujung lengan robot untuk mengetahui jarak lengan terhadap target yang akan diambil. Pengenalan benda diproses dengan menggunakan metode image-processing berdasarkan warna, lebar dan tinggi pada benda. Limit switch diletakan pada salah satu lengan gripper robot digunakan sebagai tanda bahwa benda telah digenggam. Pada penelitian ini, robot lengan mampu mengambil benda yang diperintahkan menggunakan perintah suara dengan tingkat keberhasilan sebesar 78%. ============================================================ Object picker arm robot with voice command is a robot that can be used to help human to pick the object wanted using voice command. This arm robot was applied to help patient with movement disability to pick the object wanted. Voice command recognition was processed using MFCC (Mel-Frequency Cepstrum Coefficient) and ANN (Artificial Neural Network) method. The arm robot was also equipped with camera to detect the object. Ultrasonic sensor was placed at the end of the arm robot to measure the distance between the arm and the target. Object recognition was processed using image-processing method based on color, width, and height of the object. Limit switch was placed in one of the gripper arm of the robot and used as an indicator when the object was held. In this research, the arm robot was able to pick the object commanded using voice command with success rate of 78%

Speech/non-speech discrimination based on contextual information integrated bispectrum lrt

Abstract—This letter shows an effective statistical voice activity detection algorithm based on the integrated bispectrum, which is defined as a cross spectrum between the signal and its square and inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: 1) its computation as a cross spectrum leads to significant computational savings, and 2) the variance of the estimator is of the same order as that of the power spectrum estimator. The decision rule is formulated in terms of an average likelihood ratio test (LRT) involving successive integrated bispectrum speech features. With these and other innovations, the proposed method reports significant improvements in speech/pause discrimination as well as in speech recognition over standardized techniques such as ITU-T G.729, ETSI AMR, and AFE VADs, and over recently published VADs. Index Terms—Contextual likelihood ratio test, higher order statistics, robust speech recognition, voice activity detection. I