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Particle Swarm Optimization to Improve Robustness of Distributed Speech Recognition

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This paper investigates a Particle Swarm Optimization (PSO) approach that aims at improving the performance of Distributed Speech Recognition (DSR) systems in noisy mobile environments. PSO is used to optimize the combination of Mel-Frequency based input features by estimating the best weighting of the feature stream exponents of Hidden Markov Models with Gaussian mixture emissions (GMM/HMMs). The experiments have been carried out using a subset of artificially distorted TIMIT database and have shown an important improvement that reaches a relative average of 30.97% in terms of word recognition accuracy compared to conventional DSR systems.
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Document Type: Research Article

Publication date: July 1, 2017

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  • Acta Acustica united with Acustica, published together with the European Acoustics Association (EAA), is an international, peer-reviewed journal on acoustics. It publishes original articles on all subjects in the field of acoustics, such as general linear acoustics, nonlinear acoustics, macrosonics, flow acoustics, atmospheric sound, underwater sound, ultrasonics, physical acoustics, structural acoustics, noise control, active control, environmental noise, building acoustics, room acoustics, acoustic materials, acoustic signal processing, computational and numerical acoustics, hearing, audiology and psychoacoustics, speech, musical acoustics, electroacoustics, auditory quality of systems. It reports on original scientific research in acoustics and on engineering applications. The journal considers scientific papers, technical and applied papers, book reviews, short communications, doctoral thesis abstracts, etc. In irregular intervals also special issues and review articles are published.
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