Comparison of Feature Extraction Mel Frequency Cepstral Coefficients and Linear Predictive Coding in Automatic Speech Recognition for Indonesian
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This study intends to evaluate to which extent Mel-Frequency Cepstral Coefficients (MFCC) features, besides Teager energy feature, derived from Indonesian speech signal
Pada sistem penelitian ini digunakan metode MFCC (mel frequency cepstrum coefficients) dimana MFCC ini mampu menangkap karakteristik pengenalan suara manusia atau dengan kata
Rizal Isnanto “Aplikasi pengenalan ucapan dengan ekstraksi Mel Frequency Cepstrum Coefficients (MFCC) melalui jaringan syaraf tiruan (JST) Learning Vektor. Quantization (LVQ)
Abstract— Since the parameterization in the perceptually relevant aspects of short-term speech spectra in ASR front-end is advantageous for speech recognition, such as Mel-LPC,
CONCLUSION This research aims to detect heart attack or myocardial infarction using DWT Discrete Wavelet Transform, MFCC Mel Frequency Cepstral Coefficients, and Entropy feature
72 4.3.2 CCA, MFCC and W-DFT In audio feature analysis, the cepstral coefficients of the maqamat speech signal are used as parameters to extract significant features of the maqamat
In order to minimize the obstacle and to ease the learning process we implement speech recognition techniques based on Mel Frequency Cepstral Coefficient MFCC features and Gaussian
KESIMPULAN Berdasarkan hasil penelitian yang telah dilakukan Penerapan Metode Mel Frequency Cepstral Coefficients Pada Sistem Pengenalan Suara Berbasis Desktop, maka diperoleh
