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Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s). [ABSTRACT FROM AUTHOR]

This paper presents a post-processing speech restoration module for enhancing the performance of conventional speech enhancement methods. The restoration module aims to retrieve parts of speech spectrum that may be lost to noise or suppressed when using conventional speech enhancement methods. The proposed restoration method utilizes a harmonic plus noise model (HNM) of speech to retrieve damaged speech structure. A modified HNM of speech is proposed where, instead of the conventional binary labeling of the signal in each subband as voiced or unvoiced, the concept of harmonicity is introduced which is more adaptable to the codebook mapping method used in the later stage of enhancement. To restore the lost or suppressed information, an HNM codebook mapping technique is proposed. The HNM codebook is trained on speaker-independent speech data. To reduce the sensitivity of the HNM codebook to speaker variability, a spectral energy normalization process is introduced. The proposed post-processing method is tested as an add-on module with several popular noise reduction methods. Evaluations of the performance gain obtained from the proposed post-processing are presented and compared to standard speech enhancement systems which show substantial improvement gains in perceptual quality. [ABSTRACT FROM AUTHOR]

In this paper, the probability distribution functions (pdf's) of the formant spaces of three major accents of the English language, namely, British Received Pronunciation (RP), General American, and Broad Australian, are modeled and compared. The statistical differences across the formant spaces of these accents are employed for accent conversion. An improved formant tracking method, based on linear prediction (LP) feature analysis and a two-dimensional hidden Markov model (2-D-HMM) of format trajectories, is used for estimation of the formant trajectories of vowels and diphthongs of each accent. Comparative analysis of the formant spaces of the three accents indicates that these accents are partly conveyed by the differences of the formants of vowels. The estimates of the probability distributions of the formants for each accent are used in a speech synthesis system for accent conversion. Accent synthesis, through modification of the acoustic parameters of speech, provides a means of assessing the perceptual contribution of each formant parameter on conveying an accent. The results of perceptual evaluations of accent conversion illustrate that formants play an important role in conveying accents. [ABSTRACT FROM AUTHOR]