Scholarlink

<p>Feature extraction is an essential phase in the development of Automatic Speech Recognition (ASR) systems. This study examines the performance of different deep neural network architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and (bi-LSTM) models for the Amazigh speech recognition system. When applied a several of feature extraction techniques, specifically Mel-Frequency Cepstral Coefficients (MFCC), Spectrograms, and Mel-Spectrograms, on the performance of different. The results show that the Bi-LSTM with Spectrograms achieved a maximum accuracy of 85%, giving the best performance in our Amazigh Speech Recognition (ASR) study. and we show that each feature type offers specific advantages, influenced by the particular neural network architecture employed.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The impact of MFCC, spectrogram, and Mel-Spectrogram on deep learning models for Amazigh speech recognition system

  • 期刊论文

  • 发表日期：

    2025年3月26日

查看全文
  • Meryam Telmem,
  • Naouar Laaidi,
  • Hassan Satori

摘要

Feature extraction is an essential phase in the development of Automatic Speech Recognition (ASR) systems. This study examines the performance of different deep neural network architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and (bi-LSTM) models for the Amazigh speech recognition system. When applied a several of feature extraction techniques, specifically Mel-Frequency Cepstral Coefficients (MFCC), Spectrograms, and Mel-Spectrograms, on the performance of different. The results show that the Bi-LSTM with Spectrograms achieved a maximum accuracy of 85%, giving the best performance in our Amazigh Speech Recognition (ASR) study. and we show that each feature type offers specific advantages, influenced by the particular neural network architecture employed.