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Robust speech recognition based on multi-stream processing

Publications associées (74)

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Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

Vassily Hatzimanikatis, Julien Racle, Adam Stefaniuk

Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA pol ...

Amer Inst Physics2015

Sparse Gammatone Signal Model Predicts Perceived Noise Intrusiveness

Hervé Bourlard, Raphaël Marc Ullmann

Is it possible to predict the intrusiveness of background noise in speech signals as perceived by humans? Such a question is important to the automatic evaluation of speech enhancement systems, including those designed for new wideband speech telephony, an ...

Idiap2014

Robust Log-Energy Estimation and its Dynamic Change Enhancement for In-car Speech Recognition

Hervé Bourlard, Weifeng Li

The log-energy parameter, typically derived from a full-band spectrum, is a critical feature commonly used in automatic speech recognition (ASR) systems. However, log-energy is difficult to estimate reliably in the presence of background noise. In this pap ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Bits from Photons

Feng Yang

The trends in the design of image sensors are to build sensors with low noise, high sensitivity, high dynamic range, and small pixel size. How can we benefit from pixels with small size and high sensitivity? In this dissertation, we study a new image senso ...

EPFL2012

Phase AutoCorrelation (PAC) features for noise robust speech recognition

Hynek Hermansky, Hemant Misra, Shajith Ikbal

In this paper, we introduce a new class of noise robust features derived from an alternative measure of autocorrelation representing the phase variation of speech signal frame over time. These features, referred to as Phase AutoCorrelation (PAC) features i ...

2012

Progress report of a project in very low bit-rate speech coding

Petr Motlicek, Philip Neil Garner, Milos Cernak

Background work in various levels of speech coding is reviewed, including unconstrained coding and recognition-synthesis approaches that assume the signal is speech. A pilot project in HMM-TTS based speech coding is then described, in which a comparison wi ...

Idiap2012

Optimal combination rules for adaptation and learning over networks

Ali H. Sayed

Adaptive networks, consisting of a collection of nodes with learning abilities, are well-suited to solve distributed inference problems and to model various types of self-organized behavior observed in nature. One important issue in designing adaptive netw ...

IEEE2011

A novel framework for noise robust ASR using cochlear implant-like spectrally reduced speech

We propose a novel framework for noise robust automatic speech recognition (ASR) based on cochlear implant-like spectrally reduced speech (SRS). Two experimental protocols (EPs) are proposed in order to clarify the advantage of using SRS for noise robust A ...

2011

Robust Array Calibration Using Time Delays with Application to Ultrasound Tomography

Martin Vetterli, Olivier Roy, Ivana Jovanovic

Accurate calibration is a requirement of many array signal processing techniques. We investigate the calibration of a transducer array using time delays. We derive a strategy based on the mean square error criterion and discuss how time delays that are not ...

2011

Dynamic hair capture

Mark Pauly, Hao Li

The realistic reconstruction of hair motion is challenging because of hair’s complex occlusion, lack of a well-defined surface, and non-Lambertian material. We present a system for passive capture of dynamic hair performances using a set of high-speed vide ...

Tech. Rep. Technical Report TR-907-11, Princeton University2011