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

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

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

Spectral Entropy Based Feature for Robust ASR

In general, entropy gives us a measure of the number of bits required to represent some information. When applied to probability mass function (PMF), entropy can also be used to measure the ``peakiness'' of a distribution. In this paper, we propose using the entropy of short time Fourier transform spectrum, normalised as PMF, as an additional feature for automatic speech recognition (ASR). It is indeed expected that a peaky spectrum, representation of clear formant structure in the case of voiced sounds, will have low entropy, while a flatter spectrum corresponding to non-speech or noisy regions will have higher entropy. Extending this reasoning further, we introduce the idea of multi-band/multi-resolution entropy feature where we divide the spectrum into equal size sub-bands and compute entropy in each sub-band. The results presented in this paper show that multi-band entropy features used in conjunction with normal cepstral features improve the performance of ASR system