Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...

Abstract The noise in the industrial production environment is more complex than application scenario home or office environment, and most of loudness much larger. Automatic speech recognition(ASR) system will fail when front-end signal processing SNR too poor. At this time ability particularly important. product solution mentioned article involves multiple point. It includes omnidirectional Mi...

This paper describes a hands-free speech recognition method based on HMM composition and separation for speech contaminated not only by additive noise but also by an acoustic transfer function. The method re­ alizes an improved user interface such that a user is not encumbered by microphone equipment in noisy and re­ verberant environments. In this approach， an attempt is made to model acoustic...

growing and commercially most promising applications of natural language technology. The technology has achieved a point where carefully designed systems for suitably constrained applications are a reality. Commercial systems are available today for such tasks as large-vocabulary dictation and voice control of medical equipment. This article reviews how state-of-the-art speech-recognition syste...

A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...