Journal ArticleDOI
An effective cluster-based model for robust speech detection and speech recognition in noisy environments
TLDR
An accurate speech detection algorithm for improving the performance of speech recognition systems working in noisy environments based on a hard decision clustering approach where a set of prototypes is used to characterize the noisy channel.Abstract:
This paper shows an accurate speech detection algorithm for improving the performance of speech recognition systems working in noisy environments. The proposed method is based on a hard decision clustering approach where a set of prototypes is used to characterize the noisy channel. Detecting the presence of speech is enabled by a decision rule formulated in terms of an averaged distance between the observation vector and a cluster-based noise model. The algorithm benefits from using contextual information, a strategy that considers not only a single speech frame but also a neighborhood of data in order to smooth the decision function and improve speech detection robustness. The proposed scheme exhibits reduced computational cost making it adequate for real time applications, i.e., automated speech recognition systems. An exhaustive analysis is conducted on the AURORA 2 and AURORA 3 databases in order to assess the performance of the algorithm and to compare it to existing standard voice activity detection (VAD) methods. The results show significant improvements in detection accuracy and speech recognition rate over standard VADs such as ITU-T G.729, ETSI GSM AMR, and ETSI AFE for distributed speech recognition and a representative set of recently reported VAD algorithms.read more
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Book ChapterDOI
Voice Activity Detection. Fundamentals and Speech Recognition System Robustness
TL;DR: This chapter shows a comprehensive approximation to the main challenges in voice activity detection, the different solutions that have been reported in a complete review of the state of the art and the evaluation frameworks that are normally used.
Journal ArticleDOI
Computer-aided diagnosis of Alzheimer's type dementia combining support vector machines and discriminant set of features
Javier Ramírez,Juan Manuel Górriz,Diego Salas-Gonzalez,A. Romero,M. López,I. Álvarez,Manuel Gómez-Río +6 more
TL;DR: A fully automatic computer-aided diagnosis (CAD) system for improving the early detection of the AD and outperforms existing techniques including the voxel-as-features (VAF) approach is shown.
Journal ArticleDOI
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Ignacio A. Illán,Juan Manuel Górriz,Javier Ramírez,Diego Salas-Gonzalez,M. M. López,Fermín Segovia,R. Chaves,Manuel Gómez-Río,Carlos G. Puntonet +8 more
TL;DR: This work develops a fully automatic computer aided diagnosis (CAD) system for high-dimensional pattern classification of baseline ^1^8F-FDG PET scans from Alzheimer's disease neuroimaging initiative (ADNI) participants.
Journal ArticleDOI
GMM based SPECT image classification for the diagnosis of Alzheimer's disease
TL;DR: This work presents a novel classification method of SPECT images based on Gaussian mixture models (GMM) for the diagnosis of Alzheimer's disease and shows that for various classifiers the GMM-based method yields higher accuracy rates than the classification considering all voxel values.
Journal ArticleDOI
Classification of functional brain images using a GMM-based multi-variate approach
Fermín Segovia,Juan Manuel Górriz,Javier Ramírez,Diego Salas-Gonzalez,I. Álvarez,Miriam Romero López,R. Chaves,Pablo Padilla +7 more
TL;DR: The leave-one-out cross-validation technique is used to validate the results obtained by the supervised learning-based computer aided diagnosis (CAD) system over databases of SPECT and PET images yielding an accuracy rate up to 96.67%.
References
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