Band-pass filtering of the time sequences of spectral parameters for robust wireless speech recognition

Vicente-Peña, Jesús; Gallardo-Antolín, Ascensión; Peláez-Moreno, Carmen; Díaz-de-María, Fernando

doi:10.1016/j.specom.2006.07.007

Cited by 18 publications

(8 citation statements)

References 20 publications

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“…These results are similar to those achieved by other systems that use the same database but different parameterization (e. g. Vicente-Peña et al (2006b), Woodland et al (1994) or Parihar and Picole (2001)). In the remainder of this section, we show the performance of our proposals for each database under noisy conditions.…”

Section: Resultssupporting

confidence: 87%

Uncertainty decoding on Frequency Filtered parameters for robust ASR

Vicente-Peña

Díaz-de-María

2010

Speech Communication

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The use of feature enhancement techniques to obtain estimates of the clean parameters is a common approach for robust automatic speech recognition (ASR). However, the decoding algorithm typically ignores how accurate these estimates are. Uncertainty decoding methods incorporate this type of information. In this paper, we develop a formulation of the uncertainty decoding paradigm for Frequency Filtered (FF) parameters using spectral subtraction as a feature enhancement method. Additionally, we show that the uncertainty decoding method for FF parameters admits a simple interpretation as a spectral weighting method that assigns more importance to the most reliable spectral components.Furthermore, we suggest combining this method with SSBD-HMM (Spectral Subtraction and Bounded Distance HMM), one recently proposed technique that is able to compensate for the effects of features that are highly contaminated (outliers). This combination pursues two objectives: to improve the results achieved by uncertainty decoding methods and to determine which part of the improvements is due to compensating for the effects of outliers and which part is due to compensating for other less deteriorated features.

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Section: Resultssupporting

confidence: 87%

Uncertainty decoding on Frequency Filtered parameters for robust ASR

Vicente-Peña

Díaz-de-María

2010

Speech Communication

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“…In particular, the Spectral Analysis stage in the MFCC computation is replaced by two different steps in the LP-MFCC case: Pole Modeling and Spectrum Envelope Computation (see [53] for further details).…”

Section: Front-end Descriptionmentioning

confidence: 99%

Robust ASR using Support Vector Machines

Solera-Ureña

Martín-Iglesias

Gallardo-Antolín

et al. 2007

Speech Communication

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The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units.In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM-SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in [1] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.

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“…The Gilbert model was found in [3] to be inadequate for simulating a GSM channel and instead a two-fold stochastic model is used in which there are two processes, namely shadowing and Rayleigh fading. This same model was used by [32], again to model a GSM network. Reference [33] compares three models of packet loss and examines their effectiveness at simulating different packet loss conditions.…”

Section: Channel Models and Loss Compensationmentioning

confidence: 99%