We propose an information theoretic framework for quantitative assessment of acoustic modeling for hidden Markov model (HMM) based automatic speech recognition (ASR). Acoustic modeling yields the probabilities of HMM sub-word states for a short temporal window of speech acoustic features. We cast ASR as a communication channel where the input sub-word probabilities convey the information about ...

Prosody is an important cue for identifying dialog acts. In this paper, we show that modeling the sequence of acoustic– prosodic values as n-gram features with a maximum entropy model for dialog act (DA) tagging can perform better than conventional approaches that use coarse representation of the prosodic contour through summative statistics of the prosodic contour. The proposed scheme for expl...

We propose a fully three-dimensional wavelet-based coding system featuring 3D encoding/2D decoding functionalities. A fully threedimensional transform is combined with context adaptive arithmetic coding; 2D decoding is enabled by encoding every 2D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2D image of the dataset. Fast access to 2D i...

Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches this domain use recurrent and/or convolutional models capture the spatio-temporal features time-series data multiple sensors. We propose a deep neural network architecture that not only captures of sensor but also selects, learns important time poi...

Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks w...