An American Sign Language (ASL) recognition system is being developed using artificial
neural networks (ANNs) to translate ASL words into English. The system uses a sensory …

To identify the localization of indoor sound source, especially when attempted using only a
single microphone, it is a challenging problem to machine learning. To address these …

In this paper, we describe the recent trend in automatic speech recognition. First, we should
point out that the current art of speech recognition by machines is admittedly inferior to the …

This paper describes a hands-free speech recognition technique based on acoustic model
adaptation to reverberant speech. In hands-free speech recognition, the recognition …

This communication presents a new method for automatic speech recognition in reverberant
environments. Our approach consists in the selection of the best acoustic model out of a …

This thesis examines techniques to improve the robustness of automatic speech recognition
(ASR) systems against noise distortions. The study is important as the performance of ASR …

We propose robust distant speech recognition by combining multiple microphone-array
processing with position-dependent cepstral mean normalization (CMN). In the recognition …

In distant-talking scenarios, automatic speech recognition (ASR) is hampered by
background noise, competing speakers and room reverberation. Unlike background noise …

This paper presents a sound source (talker) localization method using only a single
microphone, where a Gaussian Mixture Model (GMM) of clean speech is introduced to …

In real acoustic environments, humans communicate with each other through speech by
focusing on the target speech among environmental sounds. We can easily identify the …