Noise Robust Keyword Spotting Using Deep Neural Networks For Embedded Platforms

The recent development of embedded platforms along with spectacular growth in communication networking technologies is driving the Internet of things to thrive. More complex tasks are now possible to operate in small devices such as speech recognition and keyword spotting which are in great demand. Traditional voice recognition approaches are already being used in several embedded applications, some are hybrid(cloud-based and embedded) while others are fully embedded. However, the environment surrounding the embedded devices is usually accompanied by noise. Conventional approaches to add noise robustness to speech recognition are effective but also costly in terms of memory consumption and hardware complexities which limit their use in embedded platforms. The purpose of this thesis is to increase the robustness of keyword spotting to more than one type of noise at once without increasing the memory footprint or the need for a denoiser while maintaining the recognition accuracy to an acceptable level. In this work, robustness in treated at the phoneme classification level as the phoneme based keyword spotting is the best technique for embedded keyword spotting. Deep Neural Networks have been successfully deployed in many applications including noise robust speech recognition. In this work, we use mutil-condition utterances training of a Deep Neural Networks model to increase the keyword spotting noise robustness. This technique is also used for a Gaussian mixture model training. The two approaches are compared and the deep learning proved to not only outperform the Gaussian approach, but has also outperformed the use of a denoiser system. This results in a smaller, more accurate and noise robust model for phoneme recognition.