Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Abstract

Voice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components.

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

“…Finally, speech recognition methods have been extensively used for medical purposes and disease diagnostics, such as developing biosignal sensors to help people with disabilities speak [36] and fake news to manage sentiments [37]. The audio challenges [38] were captured using two microphone channels from an acoustic cardioid and a smartphone, allowing the performance of different types of microphones to be evaluated.…”

Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods

“…Although, DL architectures have been successfully applied to image recognition [ 43 , 44 , 45 ] and speech signal recognition [ 46 , 47 , 48 ], their use for EEG signal recognition tasks, such as imagined speech [ 49 , 50 ], remains a challenge and requires the development of novel pre-processing techniques and the development of new DL structures and architectures [ 51 , 52 ]. Among the difficulties posed by DL algorithms are: CNN methods are susceptible to the effect of artifacts present in EEG signals, generating a reduction in the accuracy of the classifiers [ 27 ].…”

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

“…The aforementioned observation was proven by several researchers [3][4][5][6][7][8][9][10][11][12][13][14]. DL is beneficial in other fields, including target recognition [15], speech recognition [16,17], image recognition [18][19][20], image restoration [21][22][23], audio classification [24,25], object detection [26][27][28][29][30], scene recognition [31], etc., but it has been considered "bad news" in text-based CAPTCHAs, by penetrating their security and making them vulnerable.…”