A dynamic automatic noisy speech recognition system for a single-channel hybrid noisy industrial environment

2013 ◽  
Vol 133 (5) ◽  
pp. 3300-3300
Author(s):  
Sheuli Paul
Keyword(s):  
Speech Recognition ◽  
Single Channel ◽  
Recognition System ◽  
Speech Recognition System ◽  
Noisy Speech ◽  
Industrial Environment ◽  
Noisy Speech Recognition

scholarly journals End-to-End Noisy Speech Recognition Using Fourier and Hilbert Spectrum Features

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1157 ◽  
Author(s):  
Daria Vazhenina ◽  
Konstantin Markov
Keyword(s):  
Speech Recognition ◽  
Speech Processing ◽  
Data Augmentation ◽  
Recognition System ◽  
Noise Robustness ◽  
Hilbert Spectrum ◽  
Noisy Speech ◽  
Hilbert Huang Transform ◽  
Noisy Speech Recognition ◽  
Non Stationary Signal

Despite the progress of deep neural networks over the last decade, the state-of-the-art speech recognizers in noisy environment conditions are still far from reaching satisfactory performance. Methods to improve noise robustness usually include adding components to the recognition system that often need optimization. For this reason, data augmentation of the input features derived from the Short-Time Fourier Transform (STFT) has become a popular approach. However, for many speech processing tasks, there is an evidence that the combination of STFT-based and Hilbert–Huang transform (HHT)-based features improves the overall performance. The Hilbert spectrum can be obtained using adaptive mode decomposition (AMD) techniques, which are noise-robust and suitable for non-linear and non-stationary signal analysis. In this study, we developed a DeepSpeech2-based recognition system by adding a combination of STFT and HHT spectrum-based features. We propose several ways to combine those features at different levels of the neural network. All evaluations were performed using the WSJ and CHiME-4 databases. Experimental results show that combining STFT and HHT spectra leads to a 5–7% relative improvement in noisy speech recognition.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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