Robust Speech Activity Detection and Direction of Arrival Using Convolutional Neural Networks

Sammanfattning: Social robots are becoming more and more common in our everyday lives. In the field of conversational robotics, the development goes towards socially engaging robots with humanlike conversation. This project looked into one of the technical aspects when recognizing speech, videlicet speech activity detection (SAD). The presented solution uses a convolutional neural network (CNN) based system to detect speech in a forward azimuth area. The project used a dataset from FestVox, called CMU Artic and was complimented by adding recorded noises. A library called Pyroomacoustics were used to simulate a real world setup to create a robust system. A simplified version was built, this model only detected speech activity and a accuracy of 95%was reached. The finished model resulted in an accuracy of 93%.It was compared with similar project, a voice activity detection(VAD) algorithm WebRTC with beamforming, as no previous published solutions to our project was found. Our solution proved to be higher in accuracy in both cases, compared to the accuracy WebRTC achieved on our dataset.