Metode Wavelet-MFCC dan Korelasi dalam Pengenalan Suara Digit

Abstract

Voice is the sound emitted from living things. With the development of Automatic Speech Recognition (ASR) technology, voice can be used to make it easier for humans to do something. In the ASR extraction process the features have an important role in the recognition process. The feature extraction methods that are commonly applied to ASR are MFCC and Wavelet. Each of them has advantages and disadvantages. Therefore, this study will combine the wavelet feature extraction method and MFCC to maximize the existing advantages. The proposed method is called Wavelet-MFCC. Voice recognition method that does not use recommendations. Determination of system performance using the Word Recoginition Rate (WRR) method which is validated with the K-Fold Cross Validation with the number of folds is 5. The research dataset used is voice recording digits 0-9 in English. The results show that the digit speech recognition system that has been built gives the highest average value of 63% for digit 4 using wavelet daubechies DB3 and wavelet dyadic transform method. As for the comparison results of the wavelet decomposition method used, that the use of dyadic wavelet transformation is better than the wavelet package.

Exploration of Spontaneous Speech Corpus Development in Urban Agriculture Instructional Videos

“…Automatic speech recognition (ASR) is a technology that enables interaction between humans and computers through voice (Dyarbirru and Hidayat, 2020). Google voice search applies an example of ASR technology that converts voice into text to perform everyday commands on mobile devices.…”

“…Common Voice is a multilingual dataset of transcribed, community-based, Creative Commons Zero (CC0) licensed audio talks built by Mozilla (Handoko and Suyanto, 2019). The Common Voice Indonesian dataset consists of 54 unique voices with a total of 5 hours of speech and 4 hours of validation (Dyarbirru and Hidayat, 2020). The data obtained is the result of crowdsourcing, for several languages the Mozilla Deep Speech and Common Voice models produce an average CER improvement of 5.99 ± 5.48 (Tachbelie et al, 2022).…”