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Found 6 papers, 0 papers with code
Information Theoretic Analysis of DNN-HMM Acoustic Modeling
We propose an information theoretic framework for quantitative assessment of acoustic modeling for hidden Markov model (HMM) based automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training.
Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding
Segmental errors are further propagated to optional suprasegmental (such as syllable) information coding.
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces.
On Structured Sparsity of Phonological Posteriors for Linguistic Parsing
Building on findings from converging linguistic evidence on the gestural model of Articulatory Phonology as well as the neural basis of speech perception, we hypothesize that phonological posteriors convey properties of linguistic classes at multiple time scales, and this information is embedded in their support (index) of active coefficients.
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees
This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available.
