Publication

Visual processing-inspired Fern-Audio features for Noise-Robust Speaker Verification

2009
Reports

Abstract

In this paper, we consider the problem of speaker verification as a two-class object detection problem in computer vision, but the object instances are 1-D short-time spectral vectors obtained from the speech signal. More precisely, we investigate the general problem of speaker verification in the presence of additive white Gaussian noise, which we consider as analogous to visual object detection under varying illumination conditions. Inspired by their recent success in illumination-robust object detection, we apply a certain class of binary-valued pixel-pair based features called Ferns for noise-robust speaker verification. Intensive experiments on a benchmark database according to a standard evaluation protocol have shown the advantage of the proposed features in the presence of moderate to extremely high amounts of additive noise.

Official source

https://infoscience.epfl.ch/entities/publication/475f886c-e4af-4572-b124-51e629ac187c

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2009
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/475f886c-e4af-4572-b124-51e629ac187c

About this result

Related concepts (31)

Additive white Gaussian noise

Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics: Additive because it is added to any noise that might be intrinsic to the information system. White refers to the idea that it has uniform power spectral density across the frequency band for the information system. It is an analogy to the color white which may be realized by uniform emissions at all frequencies in the visible spectrum.

Gaussian noise

In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). In other words, the values that the noise can take are Gaussian-distributed. The probability density function of a Gaussian random variable is given by: where represents the grey level, the mean grey value and its standard deviation.

Noise (signal processing)

In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise. Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters.

Related publications (32)

Visual processing-inspired Fern-Audio features for Noise-Robust Speaker Verification

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