Continuous speech recognition with sparse coding

TL;DR: This paper proposes a new online implementation of CNMF and CNSC which processes input data piece-by-piece and updates learned patterns gradually with accumulated statistics and shows that, with unlimited data and computing resources, the new online learning algorithm almost surely converges to a local minimum of the objective cost function.

TL;DR: In this article, the authors propose new algorithms for the local optimization of bound constrained quadratic programs, the solution of general definite quadrastic programs, and finding either a point satisfying given linear equations and inequalities or a certificate of infeasibility.

TL;DR: An online algorithm is proposed for CNMF and CNSC, which processes input data piece-by-piece and updates the learned patterns after the processing of each piece by using accumulated sufficient statistics, thereby enabling its application to large scale tasks.

TL;DR: A novel method first finds out the atoms which represent the noise sparsely, and then selectively ignores them in the reconstruction of speech to reduce the residual noise, and speech features are then extracted from the enhanced spectrum for speech recognition.

TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.

TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.

TL;DR: A circular cribbage board having a circular base plate on which a circular counter disc, bearing a circular scale having 122 divisions numbered consecutively from 0, is mounted for rotation.

TL;DR: These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.