Distributed Functional Scalar Quantization Simplified

Distributed functional scalar quantization (DFSQ) theory provides optimality conditions and predicts performance of data acquisition systems in which a computation on acquired data is desired. We address two limitations of previous works: prohibitively expensive decoder design and a restriction to sources with bounded distributions. We rigorously show that a much simpler decoder has equivalent asymptotic performance as the conditional expectation estimator previously explored, thus reducing decoder design complexity. The simpler decoder has the feature of decoupled communication and computation blocks. Moreover, we extend the DFSQ framework with the simpler decoder to acquire sources with infinite-support distributions such as Gaussian or exponential distributions. Finally, through simulation results we demonstrate that performance at moderate coding rates is well predicted by the asymptotic analysis, and we give new insight on the rate of convergence

Distributed Scalar Quantization for Computing: High-Resolution Analysis and Extensions

Communication of quantized information is frequently followed by a computation. We consider situations of \emph{distributed functional scalar quantization}: distributed scalar quantization of (possibly correlated) sources followed by centralized computation of a function. Under smoothness conditions on the sources and function, companding scalar quantizer designs are developed to minimize mean-squared error (MSE) of the computed function as the quantizer resolution is allowed to grow. Striking improvements over quantizers designed without consideration of the function are possible and are larger in the entropy-constrained setting than in the fixed-rate setting. As extensions to the basic analysis, we characterize a large class of functions for which regular quantization suffices, consider certain functions for which asymptotic optimality is achieved without arbitrarily fine quantization, and allow limited collaboration between source encoders. In the entropy-constrained setting, a single bit per sample communicated between encoders can have an arbitrarily-large effect on functional distortion. In contrast, such communication has very little effect in the fixed-rate setting.Comment: 36 pages, 10 figure

New Entropy Based Combination Rules in HMM/ANN Multi-stream ASR

Classifier performance is often enhanced through combining multiple streams of information. In the context of multi-stream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution output from each ANN, which generally increases as classification becomes less reliable. The rule most commonly used is to select the ANN with the minimum entropy. However, this is not necessarily the best way to use entropy in classifier combination. In this article, we test three new entropy based combination rules in a full-combination multi-stream HMM/ANN system for noise robust speech recognition. Best results were obtained by combining all the classifiers having entropy below average using a weighting proportional to their inverse entropy

Mel-Cepstrum Modulation Spectrum (MCMS) Features for Robust ASR

In this paper, we present new dynamic features derived from the modulation spectrum of the cepstral traje ctories of the speech signal. Cepstral trajectories are projected over the basis of sines and cosines yie lding the cepstral modulation frequency response of the speech signal. We show that the different sines a nd cosines basis vectors select different modulation frequencies, whereas, the frequency responses of the delta and the double delta filters are only centered over 15Hz. Therefore, projecting cepstral trajector ies over the basis of sines and cosines yield a more complementary and discriminative range of features. In this work, the cepstrum reconstructed from the lower cepstral modulation frequency components is used as the static feature. In experiments, it is shown that, as well as providing an improvement in clean co nditions, these new dynamic features yield a significant increase in the speech recognition performance in various noise conditions when compared directly to the standard temporal derivative features and C-JRASTA PLP features

On Factorizing Spectral Dynamics for Robust Speech Recognition

In this paper, we introduce new dynamic speech features based on the modulation spectrum. These features, termed Mel-cepstrum Modulation Spectrum (MCMS), map the time trajectories of the spectral dynamics into a series of slow and fast moving orthogonal components, providing a more general and discriminative range of dynamic features than traditional delta and acceleration features. The features can be seen as the outputs of an array of band-pass filters spread over the cepstral modulation frequency range of interest. In experiments, it is shown that, as well as providing a slight improvement in clean conditions, these new dynamic features yield a significant increase in speech recognition performance in various noise conditions when compared directly to the standard temporal derivative features and RASTA-PLP features