Use of Cepstrum-based parameters for automatic pathology detection on speech. Analysis of performance and theoretical justification

The majority of speech signal analysis procedures for automatic pathology detection mostly rely on parameters extracted from time-domain processing. Moreover, calculation of these parameters often requires prior pitch period estimation; therefore, their validity heavily depends on the robustness of pitch detection. Within this paper, an alternative approach based on cepstral-domain processing is presented which has the advantage of not requiring pitch estimation, thus providing a gain in both simplicity and robustness. While the proposed scheme is similar to solutions based on Mel-frequency cepstral parameters, already present in literature, it has an easier physical interpretation while achieving similar performance standards

Use of Mel Frequency Cepstral Coefficients for Automatic Pathology Detection on Sustained Vowel Phonations: Mathematical and Statistical Justification

This paper presents a justification for the use of MFCC parameters in automatic pathology detection on speech. While such an application has produced good results up to now, only partial explanations to this good performance had been given before. The herein exposed explanation consists of an interpretation of the mathematical transformations involved in MFCC calculation and a statistical analysis that confirms the conclusions drawn from the theoretical reasoning

Analysis and Detection of Pathological Voice using Glottal Source Features

Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Principe de Asturias (HUPA) database and the Saarbrucken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features

Damage detection in a RC-masonry tower equipped with a non-conventional TMD using temperature-independent damage sensitive features

Many features used in Structural Health Monitoring strategies are not just highly sensitive to failure mechanisms, but also depend on environmental or operational fluctuations. To prevent incorrect failure uncovering due to these dependencies, damage detection approaches can use robust and temperature-independent features. These indicators can be naturally insensitive to environmental dependencies or artificially made independent. This work explores both options. Cointegration theory is used to remove environmental dependencies from dynamic features to create highly sensitive parameters to detect failure mechanisms: the cointegration residuals. This paper applies the cointegration technique for damage detection of a concrete-masonry tower in Italy. Two regression models are implemented to capture temperature effects: Prophet and Long Short-Term Memory networks. Results demonstrate the advantages and limitations of this methodology for real applications. The authors suggest to combine the cointegration residuals with a secondary temperature-insensitive damage-sensitive set of features, the Cepstral Coefficients, to address the possibility of capturing undetected structural damage

Objective Estimation of Tracheoesophageal Speech Quality

Speech quality estimation for pathological voices is becoming an increasingly important research topic. The assessment of the quality and the degree of severity of a disordered speech is important to the clinical treatment and rehabilitation of patients. In particular, patients who have undergone total laryngectomy (larynx removal) produce Tracheoesophageal (TE) speech. In this thesis, we study the problem of TE speech quality estimation using advanced signal processing approaches. Since it is not possible to have a reference (clean) signal corresponding to a given TE speech (disordered) signal, we investigate in particular the non-intrusive techniques (also called single-ended or blind approaches) that do not require a reference signal to deduce the speech quality level. First, we develop a novel TE speech quality estimation based on some existing double-ended (intrusive) speech quality evaluation techniques such as the Perceptual Evaluation Speech Quality (PESQ) and Hearing Aid Speech Quality Index HASQI. The matching pursuit algorithm (MPA) was used to generate a quasi-clean speech signal from a given disordered TE speech signal. Then, by adequately choosing the parameters of the MPA (atoms, number of iterations,...etc) and using the resulting signal as our reference signal in the intrusive algorithm, we show that the resulting intrusive algorithm correlates well with the subjective scores of two TE speech databases. Second, we investigate the extraction of low complexity auditory features for the evaluation of speech quality. An 18-th order Linear Prediction (LP) analysis is performed on each voiced frame of the speech signal. Two evaluation features are extracted corresponding to higher-order statistics of the LP coefficients and the vocal tract model parameters (cross-sectional tubes areas). Using a set of 35 TE speech samples, we perform forward stepwise regression as well as K-fold cross-validation to select the best sets of features that are used in each of the regression models. Finally, the selected features are fitted to different support vector regression models yielding high correlations with subjective scores. Finally, we investigate a new approach for the estimation of the quality of TE speech using deep neural networks (DNNs). A synthetic dataset that consists of 2173 samples was used to train a DNN model that was shown to predict the TE voice quality. The synthetic dataset was formed by mixing 53 normal speech samples with modulated noise signals that had a similar envelope to the speech samples, at different speech-to-modulation noise ratios. A validated instrumental speech quality predictor was used to quantify the perceived quality of speech samples in this database, and these objective quality scores were used for training the DNN model. The DNN model was comprised of an input layer that accepted sixty relevant features extracted through filterbank and linear prediction analyses of the input speech signal, two hidden layers with 15 neurons each, and an output layer that produced the predicted speech quality score. The DNN trained on the synthetic dataset was subsequently applied to four different databases that contained speech samples collected from TE speakers. The DNN-estimated quality scores exhibited a strong correlation with the subjective ratings of the TE samples in all four databases, thus it shows strong robustness compared to those speech quality metrics developed in this thesis or those from the literature

A Review of the Assessment Methods of Voice Disorders in the Context of Parkinson's Disease

In recent years, a significant progress in the field of research dedicated to the treatment of disabilities has been witnessed. This is particularly true for neurological diseases, which generally influence the system that controls the execution of learned motor patterns. In addition to its importance for communication with the outside world and interaction with others, the voice is a reflection of our personality, moods and emotions. It is a way to provide information on health status, shape, intentions, age and even the social environment. It is also a working tool for many, but an important element of life for all. Patients with Parkinson’s disease (PD) are numerous and they suffer from hypokinetic dysarthria, which is manifested in all aspects of speech production: respiration, phonation, articulation, nasalization and prosody. This paper provides a review of the methods of the assessment of speech disorders in the context of PD and also discusses the limitations

Development of acoustic analysis techniques for use in diagnosis of vocal pathology

Acoustic analysis as used in the vocal pathology literature has come to mean any spectrum or waveform measurement taken from the digitised speech signal. The purpose of the work as set out in the present thesis is to investigate the currently available acoustic measures, to test their validity and to introduce new measures. More specifically, pitch extraction techniques and perturbation measures have been tested, several harmonic to noise ratio techniques have been implemented and thoroughly investigated (three of which are new) and cepstral and other spectral measures have been examined. Also, ratios relevant to voice source characteristics and perceptual correlation have been considered in addition to the tradition harmonic to noise ratios. A study of these approaches has revealed that many measurement problems arise and that the separation of the indices into independent measures is not a simple issue. The most commonly used acoustic measures for diagnosis o f vocal pathology are jitter, shimmer and the harmonic to noise ratio. However, several researchers have shown that these measures are not independent and therefore may give ambiguous information. For example, the addition of random noise causes increased jitter measurements and the introduction of jitter causes a reduced harmonic to noise ratio. Recent studies have shown that the glottal waveform and hence vibratory pattern of the vocal folds may be estimated in terms of spectral measurements. However, in order to provide spectral characterisation of the vibratory pattern in pathological voice types the effects of jitter and shimmer on the speech spectrum must firstly be removed. These issues are thoroughly addressed in this thesis. The foundation has been laid for future studies that will investigate the vibratory pattern of the vocal folds based on spectral evaluation of tape recorded data. All analysis techniques are tested by initially running them on specially designed synthesis data files and on a group of 13 patients with varying pathologies and a group of twelve normals. Finally, the possibility of using digital spectrograms for speaker identification purposes has been addressed

Caractérisation des cris des nourrissons en vue du diagnostic précoce de différentes pathologies

L’utilisation des signaux de cris dans le diagnostic se base sur les théories qui ont été proposées par les différents chercheurs dans le domaine. Le principal objectif de leurs travaux était l’analyse spectrographique ainsi que la modélisation des signaux de cris. Ils ont démontré que les caractéristiques acoustiques des cris des nouveau-nés sont liées à des conditions médicales particulières. Cette thèse est destinée à contribuer à l’amélioration de la précision de la reconnaissance des cris pathologiques par la combinaison de plusieurs paramètres acoustiques issus de l'analyse spectrographique et des paramètres qui qualifient les cordes et le conduit vocal. Car les caractéristiques acoustiques représentant le conduit vocal ont été largement utilisées pour la classification des cris, alors que les caractéristiques des cordes vocales pour la reconnaissance automatique des cris, ainsi que leurs techniques efficaces d’extraction n’ont pas été exploitées. Pour répondre à cet objectif, nous avons procédé en premier lieu à une caractérisation qualitative des cris des nouveau-nés sains et malades en utilisant les caractéristiques qui ont été définies dans la littérature et qui qualifient le comportement des cordes et du conduit vocal pendant le cri. Cette étape nous a permis d’identifier les caractéristiques les plus importantes dans la différenciation des cris pathologiques étudiés. Pour l’extraction des caractéristiques sélectionnées, nous avons implémenté des méthodes de mesures efficaces permettant de dépasser la surestimation et la sous-estimation des caractéristiques. L’approche de quantification proposée et utilisée dans ce travail facilite l’analyse automatique des cris et permet une utilisation efficace de ces caractéristiques dans le système de diagnostic. Nous avons procédé aussi à des tests expérimentaux pour la validation de toutes les approches introduites dans cette thèse. Les résultats sont satisfaisants et montrent une amélioration dans la reconnaissance des cris par pathologie. Les travaux réalisés sont présentés dans cette thèse sous forme de trois articles publiés dans différents journaux. Deux autres articles publiés dans des comptes rendus de conférences avec comité de lecture sont présentés en annexes

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research