Speech has great potential as an input mechanism for ubiquitous computing. However, the current requirements necessary for accurate speech recognition, such as a quiet environment...
Katherine Everitt, Susumu Harada, Jeff A. Bilmes, ...
In this work, we present comparative evaluation of the practical value of some recently proposed speech parameterizations on the speech recognition task. Specifically, in a common...
— This paper describes the hardware architecture for a flexible probability density estimation unit to be used in a Large Vocabulary Speech Recognition System, and targeted for m...
Speech recognition techniques have been developed dramatically in recent years. Nevertheless, errors caused by environmental noise are still a serious problem in recognition. Empl...
Although speech recognition systems have become more reliable in recent years, they are still highly error-prone. Other components of a spoken language dialogue system must then b...
Speech recognition has matured over the past years to the point that companies can seriously consider its use. However, from a developer’s perspective we observe that speech inp...
Werner Kurschl, Stefan Mitsch, Rene Prokop, Johann...
In this paper, we present the design and implementation of a distributed sensor network application for embedded, isolated-word, real-time speech recognition. In our system design...
Chung-Ching Shen, William Plishker, Shuvra S. Bhat...
Hidden Markov Model (HMM) is the dominant technology in speech recognition. The problem of optimizing model parameters is of great interest to the researchers in this area. The Ba...
Non-negative matrix factorisation (NMF) is an unsupervised learning technique that decomposes a non-negative data matrix into a product of two lower rank non-negative matrices. Th...
Alexander Bertrand, Kris Demuynck, Veronique Stout...
We propose a model for speech recognition that consists of multiple semi-synchronized recognizers operating on a polyphase decomposition of standard speech features. Specifically...