Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make th ....Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make the speech and speaker recognition systems less sensitive to additive background noise and make them more useful in telecommunications and business.Read moreRead less
Perceptually-motivated speech parameters for concurrent coding and noise-robust distributed recognition of human speech for mobile telephony systems. With speech being a simple and natural form of communication, speech recognition technology is being widely used in mobile phones. Nowadays, consumers can interact with remote systems via spoken words. This project will develop remote speech recognition with better accuracy and noise-robustness while using the existing mobile phone infrastructure.