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

Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering

Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals. This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates. To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient

Activity, context, and plan recognition with computational causal behavior models

Objective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm – the Marginal Filter – is introduced

Reconhecimento automático de fala com processamento simultâneo de características acústicas e visuais

Tese de mestrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Inclusion Of Temporal Information Into Features For Speech Recognition

Conventional methods for incorporating temporal information into speech features apply regression to a series of successive cepstral vectors to generate differential cepstra, or apply a cosine transform to generate cepstral-time matrices. This paper aims to generalise these techniques such that a series of stacked cepstral vectors is multiplied by a temporal transform matrix to produce the final speech feature. This can made to incorporate both static and dynamic speech information. Using this method, the coding of temporal information is not restricted to regression or cosine coefficients - any suitable transform may used. Results are presented for a variety of transforms, such as Legendre, Karhunen-Loeve, Cosine, Rectangle, where it is shown that the transform based techniques offer higher performance than conventional differential cepstrum