Non-parametric estimation and correction of non-linear distortion in speech systems

Balchandran, Rajesh; Mammone, Richard J.

doi:10.1109/icassp.1998.675373

Cited by 22 publications

(17 citation statements)

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“…HE is a non-linear transformation that converts a probability distribution to another, in order to match all the statistics of two probability distributions, reducing the mismatch of the statistics of two signals [48,49]. This technique has also been developed for speech recognition adaptation approaches and correction of non-linear effects [50,51], and it has also been applied to the acoustic features in order to improve the robustness of a speaker verification system by reducing the mismatch between training and test conditions and the additive noise and channel and transducer effects [49,52].…”

Section: Normalisation Methodsmentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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Jitter and shimmer are measures of the fundamental frequency and amplitude cycle-to-cycle variations, respectively. Both features have been largely used for the description of pathological voices, and since they characterise some aspects concerning particular voices, they are expected to have a certain degree of speaker specificity. In the current work, jitter and shimmer are successfully used in a speaker verification experiment. Moreover, both measures are combined with spectral and prosodic features using several types of normalisation and fusion techniques in order to obtain better verification results. The overall speaker verification system is also improved by using histogram equalisation as a normalisation technique previous to fusing the features by SVM.

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Section: Normalisation Methodsmentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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“…Histogram normalization can be applied either in the waveform domain (e.g. [12]), the spectral domain (e.g. [13]), or the cepstral domain (e.g.…”

Section: Introductionmentioning

confidence: 99%

Power function-based power distribution normalization algorithm for robust speech recognition

Kim

Stern

2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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Abstract-A novel algorithm that normalizes the distribution of spectral power coefficients is described in this paper. The algorithm, called power-function-based power distribution (PPDN) is based on the observation that the ratio of arithmetic mean to geometric mean changes as speech is corrupted by noise, and a parametric power function is used to equalize this ratio. We also observe that a longer "medium-duration" observation window (of approximately 100 ms) is better suited for parameter estimation for noise compensation than the briefer window typically used for automatic speech recognition. We also describe the implementation of an online version of PPDN based on exponentially weighted temporal averaging. Experimental results shows that PPDN provides comparable or slightly better results than state of-the-art algorithms such as vector Taylor series for speech recognition while requiring much less computation. Hence, the algorithm is suitable for both real-time speech communication or as a real-time preprocessing stage for speech recognition systems.

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“…This technique has been also developed for speech recognition adaptation approaches and correction of non-linear effects typically introduced by speech systems such as microphones, amplifiers, clipping and boosting circuits and automatic gain control circuits (Balchandran and Mammone, 1998;Hilger and Ney, 2001). …”

Section: Histogram Equalisationmentioning

confidence: 99%

“…This method was first designed for the enhancement of images, and it was further used for the speech recognition adaptation approaches and the correction of non-linear effects typically introduced by speech systems (Balchandran and Mammone, 1998;Hilger and Ney, 2001). The objective of this method is, in short, to find a non-linear transformation to reduce the mismatch of the statistics of two signals.…”

Section: Multimodal Fusion 45mentioning

confidence: 99%

Fusing prosodic and acoustic information for speaker recognition

Farrús

2009

The International Journal of Speech, Language and the Law

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ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author PhD Dissertation FUSING PROSODIC AND ACOUSTIC INFORMATION FOR SPEAKER RECOGNITION Mireia Farrús i Cabeceran AbstractAutomatic speaker recognition is the use of a machine to identify an individual from a spoken sentence. Recently, this technology has been undergone an increasing use in applications such as access control, transaction authentication, law enforcement, forensics, and system customisation, among others.One of the central questions addressed by this field is what is it in the speech signal that conveys speaker identity. Traditionally, automatic speaker recognition systems have relied mostly on short-term features related to the spectrum of the voice. However, human speaker recognition relies on other sources of information; therefore, there is reason to believe that these sources can play also an important role ...

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Non-parametric estimation and correction of non-linear distortion in speech systems

Cited by 22 publications

References 1 publication

Using Jitter and Shimmer in speaker verification

Using Jitter and Shimmer in speaker verification

Power function-based power distribution normalization algorithm for robust speech recognition

Fusing prosodic and acoustic information for speaker recognition

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