Daniel Palacios-Alonso

Parkinson's disease monitoring by biomechanical instability of phonation

Abstract Patients suffering from Parkinsons disease (PD) may be successfully treated pharmacologically and surgically to preserve and even improve their life quality and health conditions. Although the progress of the disease cannot be stopped, at least mitigation of the most handicapping symptoms can be achieved. But both pharmacological and surgical treatments require the adequate monitoring of the disease stage of progress and the effects of treatment. Several techniques have been proposed for PD evolution monitoring, ranging from subjective auto-evaluation by questionnaires, or from gait and handwriting examination by specialists. Nevertheless, these techniques present certain difficulties, which make frequent evaluation impractical. On the other hand, it is known that speech acoustic analysis may estimate indicators of patients conditions, and can be implemented for a frequent evaluation protocol; and under minimal help, it can be carried out at distance using communication technologies. The acoustic analysis, may be based on mel-cepstral coefficients, distortion features as jitter, shimmer, harmonic-to-noise contents, or pitch-perturbation estimates, among others. Phonation biomechanical parameter and tremor estimates are also good markers of PD. The present work proposes a combination of biomechanical features to predict PD progress using Bayesian likelihood estimation. This methodology proves to be very sensitive and allows a three-band based comparison: pre-treatment versus post-treatment in reference to a control subject or a normative population. Results from a study are presented, including eight patients recorded on a 4-week separation interval, meanwhile they were treated with medication, physical exercising and speech therapy. The conclusions show that certain distortion, biomechanical and tremor features are of special relevance to monitor PD phonation, and that they can be used as evolution markers.

Relating Facial Myoelectric Activity to Speech Formants

Speech articulation is conditioned by the movements produced by well determined groups of muscles in the larynx, pharynx, mouth and face. The resulting speech shows acoustic features which are directly related with muscle neuromotor actions. Formants are some of the observable correlates most related to certain muscle actions, such as the ones activating jaw and tongue. As the recording of speech is simple and ubiquitous, the use of speech as a vehicular tool for neuromotor action monitoring would open a wide set of applications in the study of functional grading of neurodegenerative diseases. A relevant question is how far speech correlates and neuromotor action are related. This question is answered by the present study using electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. Correlation measurements help in establishing a clear relation between the time derivative of the first formant and the masseter myoelectric activity. Monitoring disease progress by acoustic kinematics in one case of Amyotrophic Lateral Sclerosis ALS is described.

Towards the search of detection in speech-relevant features for stress

Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond trying to relate the biometrical signature of voice with a possible neural activity that might generate alterations in voice production. A total of 68, acoustical, glottal and biomechanical parameters were extracted from neutral and stressed speeches. The importance of the parameters was evaluated using t-test, entropy, Receiver Operator Characteristic ROC and Wilcoxon methods and support vector machines algorithms for classification. The emotion under study is the stress produced when a speaker has to defend an idea opposite to his/her thoughts or feelings, and this stress is compared to self-consistent speech. The results show tremor in the vocal folds to be the most relevant feature.

Parkinson Disease Detection from Speech Articulation Neuromechanics

Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech. Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set. Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females). Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease.

Speech parameter selection for emotional stress characterization in women

A total of 68 acoustical, glottal and biomechanical parameters were extracted from neutral and stressed speech from 31 women. The stress was elicited by asking the speaker to defend her false opinion in a controversial topic. The importance of the parameters was evaluated accordingly to Pearsons coefficient, and the most relevant ones were tested in a SVM classification algorithm. We have found out that the most relevant parameters to characterize stress are pitch and the biomechanical parameters of body mass and body stiffness of the vocal folds.

Emotional Stress Detection in Contradictory versus Self-consistent Speech by Means of Voice Biometrical Signature

Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond by trying to relate the biometrical signature of voice with a possible neural activity that might generate voice production. The present study affords emotional differentiation in speech from the behavior of the biomechanical stiffness and cyclicality estimates, indicators of tremor. The emotion under study is the stress produced when a speaker has to defend an idea opposite to his/her thoughts or feelings and compared when his/her speech is self-consistent. The results presented show that females tend to relax vocal folds and decrease tremor and males tend to show the opposite behavior.

Vowel Articulation Distortion in Parkinson’s Disease

Neurodegenerative pathologies produce important distortions in speech. Parkinson’s Disease (PD) leaves marks in fluency, prosody, articulation and phonation. Certain measurements based in configurations of the articulation organs inferred from formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been classically used in this sense, but these markers represent mainly the static positions of sustained vowels on the vowel triangle. The present study proposes a measurement based on the mutual information contents of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the articulation organs, involving the jaw and tongue is estimated and modelled statistically. The distribution of this feature is rather different in PD patients than in normative speakers when sustained vowels are considered. Therefore, articulation failures may be detected even in single sustained vowels. The study has processed a limited database of 40 female and 54 male PD patients, contrasted to a very selected and stable set of normative speakers. Distances based on Kullback-Leibler’s Divergence have shown to be sensitive to PD articulation instability. Correlation measurements show that the distance proposed shows statistically relevant relationship with certain motor and non-motor behavioral observations, as freezing of gait, or sleep disorders. These results point out to the need of defining scoring scales specifically designed for speech-based diagnose and monitoring methodologies in degenerative diseases of neuromotor origin.

Monitoring Parkinson’s Disease Rehabilitation from Phonation Biomechanics

Neuromotor disease rehabilitation may benefit from certain phonation tasks as singing exercises. A protocol is being designed to combine certain rehabilitation tasks, consisting in respiratory and phonation, while carrying out simple singing drills. The objective evaluation of phonation before, during and after singing exercises is conducted from the estimation of biomechanical features of the glottal source. These features are compared using information theory and quadratic entropy principles. Results from Parkinson Disease patients under the rehabilitation programme are presented and discussed.

Analysis of emotional stress in voice for deception detection

The current work gives a method to estimate alterations in speech caused by stress. This estimation is used as an indicator to detect deceptive speech. Truthful or neutral speech and deceptive speech were elicited by forcing to answer “hot questions” related with politics and society. Data were processed by using log-likelihood ratios and Fishers linear discriminant analysis. Independent results for male and female are presented. The classification results are around 100% for neutral speech in both genders, while the best classification rate (67%) for stressed speech is achieved for females. In a first approach we have seen, that subjects tend to be more deceptive when the questions are related with gender issues, giving a politically correct answer instead of their true opinion.