Performance evaluation of real-time noisy speech recognition for mobile devices

Yurtcan, Yaser
Communication is important for people. There are many available communication methods. One of the most effective methods is through the use of speech. People can comfortably express their feelings and thoughts by using speech. However, some people may have a hearing problem. Furthermore, understanding spoken words in a noisy environment could be a challenge even for healthy people. Speech recognition systems enable real-time speech to text conversion. They mainly involve capturing of the sound waves and converting them into meaningful texts. The use of speech recognition on mobile devices has been possible with the development of cloud systems. However, delivering a robust and low error rate speech recognition system in a noisy environment still is a major problem. In this study, different speech samples have been recorded using a compact microphone array in noisy environments and a data set has been created by processing them through a real-time noise cancellation algorithm. A portable design of a mobile system with noise cancellation hardware and software was proposed to convert spoken words to a meaningful text. Comprehensive tests were performed on several clean, noisy and denoised speech samples to measure the speech recognition performance of different cloud systems, noise robustness of the proposed system, the effect of gender on the speech recognition performance, and the performance improvement. The experimental results show that the proposed system provides good performance even in a noisy environment. It is also inferred from the results that in order to apply speech recognition using cloud based systems on mobile devices, the noise level has to be low or real-time noise cancellation algorithms are needed. The proposed system improves speech recognition accuracy in noisy environments. Thus, the achieved performance and portable design together enable the system to be used in daily life
Citation Formats
Y. Yurtcan, “Performance evaluation of real-time noisy speech recognition for mobile devices,” M.S. - Master of Science, Middle East Technical University, 2019.