Enhancing speech recorded from a wearable sensor using a collection of autoencoders

Abstract

Assistive Technology (AT) is a concept which includes the use of technological devices to improve the learning process or the general capabilities of people with disabilities. One of the major tasks of the AT is the development of devices that offer alternative or augmentative communication capabilities.

In this work, we implemented a simple AT device with a low-cost sensor for registering speech signals, in which the sound is perceived as low quality and corrupted. Thus, it is not suitable to integrate into speech recognition systems, automatic transcription or general recognition of vocal-tract sounds for people with disabilities.

We propose the use of a group of artificial neural networks that improve different aspects of the signal. In the study of the speech enhancement, it is normal to focus on how to make improvements in specific conditions of the signal, such as background noise, reverberation, natural noises, among others. In this case, the conditions that degrade the sound are unknown. This uncertainty represents a bigger challenge for the enhancement of the speech, in a real-life application.

The results show the capacity of the artificial neural networks to enhance the quality of the sound, under several objective evaluation measurements. Therefore, this proposal can become a way of treating these kinds of signals to improve robust speech recognition systems and increase the real possibilities for implementing low-cost AT devices.