A Particle Filter Compensation Approach to Robust Speech Recognition

Mushtaq, Aleem

doi:10.5772/51532

Cited by 2 publications

(3 citation statements)

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“…n i t−1 , that has a significant weight will split into child particles, in proportional to its weight. These particles are displaced by adding a random vector w that is sampled from the distribution in equation (13). The second choice for the state transition model is the autoregressive (AR) model.…”

Section: Noise Trackingmentioning

confidence: 99%

“…PARTICLE FILTER SAMPLE GENERATION USING HMMS In [13], samples were generated from a particular state of an HMM representing clean speech statistics. q(.)…”

Section: Particle Filter Approach To Speech Feature Compensationmentioning

confidence: 99%

“…Being a Monte Carlo method, particle filters are versatile and can handle a broad category of dynamical systems as they are not constrained by linearity and Gaussianity requirements that inhibit Kalman Filter [11] and the extended Kalman Filter [12]. Particle filter compensation (PFC) [13] [14] algorithms compensate noisy speech features by directly tracking the clean speech features in the spectral domain. The recognition is performed on melfrequency cepstral coefficient (MFCC) features extracted from the newly estimated filter bank features.…”

Section: Introductionmentioning

confidence: 99%

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Joint tracking of clean speech and noise using HMMs and particle filters for robust speech recognition

Mushtaq

Lee

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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We propose a dynamic joint tracking framework to monitor the clean speech signal and noise simultaneously in order to compensate the noisy features. The clean speech signal is tracked using an integrated algorithm based on both particle filters and hidden Markov models. The information available from speech tracking is used for tracking and estimating the noise parameters. The availability of dynamic noise information enhances the robustness of the algorithm in case of large fluctuations in noise. We report on experimental results obtained with the Aurora-2 connected digit recognition task, and show that the performance for the additive noise cases can be improved by 12.15% over the state-of-the-art multi-condition training if the noise mean is updated every 300 milliseconds.

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Section: Noise Trackingmentioning

confidence: 99%

“…PARTICLE FILTER SAMPLE GENERATION USING HMMS In [13], samples were generated from a particular state of an HMM representing clean speech statistics. q(.)…”

Section: Particle Filter Approach To Speech Feature Compensationmentioning

confidence: 99%