NeuroSpeech

NeuroSpeech is a software for modeling pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. Although it was developed to model dysarthric speech signals from Parkinson's patients, its structure allows other computer scientists or developers to include other pathologies and/or measures. Different tasks can be performed: (1) modeling of the signals considering the aforementioned speech dimensions, (2) automatic discrimination of Parkinson's vs. non-Parkinson's, and (3) prediction of the neurological state according to the Unified Parkinson's Disease Rating Scale (UPDRS) score. The prediction of the dysarthria level according to the Frenchay Dysarthria Assessment scale is also provided

Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease

Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UPDRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores

Evaluation of wavelet measures on automatic detection of emotion in noisy and telephony speech signals

Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been applied is in situations where the human integrity and security are at risk. In this paper we are propossing a set of features based on the Teager energy operator, and several entropy measures obtained from the decomposition signals from discrete wavelet transform to characterize different types of negative emotions such as anger, anxiety, disgust, and desperation. The features are measured in three different conditions: (1) the original speech signals, (2) the signals that are contaminated with noise, or are affected by the presence of a phone channel, and (3) the signals that are obtained after processing using an algorithm for Speech Enhancement based on Karhunen-Love Transform. According to the results, when the speech enhancement is applied, the detection of emotion in speech is increased in up to 22% compared to results obtained when the speech signal is highly contaminated with noise. © 2014 IEEE

Effect of ternary alloying elements additions on the structural and mechanical properties of B2 NiAl-X intermetallics

NiAl intermetallics have been widely studied due to their interesting properties, such as low density, high melting point, thermal conductivity and most importantly, very good oxidation resistance for structural applications at high temperatures [1]. The B2-NiAl system is a thermodynamically stable, ordered intermetallic that accepts ternary elements additions (e.g. Co, Cr, Mo, Pt, among others) in a wide range of compositions. Since B2-NiAl is a brittle material at low temperature ternary alloying elements additions have been intensively studied. For instance, ductility of B2-NiAl is improved by small additions of Fe, whose strengthening mechanism has been related to preferential deformation along specific slip planes directions [2]. However, a general approach about the effect of alloying elements on the structural stability and mechanical properties (e.g., phase transformations and elastic constants) of B2-NiAl during oxidation remains to be understood. In this work, a set of B2 NiAl-X (X=0, 3, 5, 7, 10, 15 and 34 at% Cr) samples with compositions were processed by high-energy ball milling from Al, Ni and Cr precursor powders. Bulk coupons were obtained by hot pressing (HP) and characterized by X-ray diffraction, scanning electron microscopy and nanoindentation. The superlattice (100), (111), (210) and fundamental (110), (200) and (211) peaks of the binary and ternary B2 phases were observed. The hardness and reduced elastic modulus were determined by nanoindentation. This work was extended on the basis of a theoretical approach to study the stability and elastic-plastic behavior of the B2-phase as a function of Co, Cr, Fe, Mo, and Pt additions in the range of their solubility limits. The theoretical calculations were performed by means of electronic structure first principle calculations of NiAl-X (X=Cr, Fe, Pt, Co, Mo) using the Spin Polarized Relativistic Koringa-Kohn Rostoker (SPRKKR) code. The lattice parameter was obtained by evaluating the calculated total energy of the crystal. The main elastic constants for cubic systems, as well as the bulk modulus were estimated by applying volume-conserving orthorhombic (C11, C12) and monoclinic (C44) deformations of the B2 lattice and fitting the total energy to the Bich-Murnaghan equation of state. The calculations show good agreement between the experimentally determined ordering and elastic-plastic behavior of the B2 phase. [1] Noebe, R.D., Bowman, R.R., and Nathal, M.V., International Materials Reviews, 1993, vol. 38, no 4, p. 193-232. [2] Darolia, R., Larman, D., and Field, R.D., Scripta Metall Mater, 1992, vol. 26, no. 7, p. 1007-101

NeuroSpeech: An open-source software for Parkinson's speech analysis

A new software for modeling pathological speech signals is presented in this paper. The software is called NeuroSpeech. This software enables the analysis of pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. All the methods considered in the software have been validated in previous experiments and publications. The current version of NeuroSpeech was developed to model dysarthric speech signals from people with Parkinson's disease; however, the structure of the software allows other computer scientists or developers to include other pathologies and/or other measures in order to complement the existing options. Three different tasks can be performed with the current version of the software: (1) the modeling of the speech recordings considering the aforementioned speech dimensions, (2) the automatic discrimination of Parkinson's vs. non-Parkinson's speech signals (if the user has access to recordings of other pathologies, he/she can re-train the system to perform the detection of other diseases), and (3) the prediction of the neurological state of the patient according to the Unified Parkinson's Disease Rating Scale (UPDRS) score. The prediction of the dysarthria level according to the Frenchay Dysarthria Assessment scale is also provided (the user can also train the system to perform the prediction of other kind of scales or degrees of severity).To the best of our knowledge, this is the first software with the characteristics described above, and we consider that it will help other researchers to contribute to the state-of-the-art in pathological speech assessment from different perspectives, e.g., from the clinical point of view for interpretation, and from the computer science point of view enabling the test of different measures and pattern recognition techniques