Victoria Rodellar-Biarge

Glottal Source biometrical signature for voice pathology detection

The Glottal Source is an important component of voice as it can be considered as the excitation signal to the voice apparatus. The use of the Glottal Source for pathology detection or the biometric characterization of the speaker are important objectives in the acoustic study of the voice nowadays. Through the present work a biometric signature based on the speakers power spectral density of the Glottal Source is presented. It may be shown that this spectral density is related to the vocal fold cover biomechanics, and from literature it is well-known that certain speakers features as gender, age or pathologic condition leave changes in it. The paper describes the methodology to estimate the biometric signature from the power spectral density of the mucosal wave correlate, which after normalization can be used in pathology detection experiments. Linear Discriminant Analysis is used to confront the detection capability of the parameters defined on this glottal signature among themselves and compared to classical perturbation parameters. A database of 100 normal and 100 pathologic subjects equally balanced in gender and age is used to derive the best parameter cocktails for pathology detection and quantification purposes to validate this methodology in voice evaluation tests. In a study case presented to illustrate the detection capability of the methodology exposed a control subset of 24+24 subjects is used to determine a subjects voice condition in a pre- and post-surgical evaluation. Possible applications of the study can be found in pathology detection and grading and in rehabilitation assessment after treatment.

Characterizing Neurological Disease from Voice Quality Biomechanical Analysis

The dramatic impact of neurological degenerative pathologies in life quality is a growing concern nowadays. Many techniques have been designed for the detection, diagnosis, and monitoring of the neurological disease. Most of them are too expensive or complex for being used by primary attention medical services. On the other hand, it is well known that many neurological diseases leave a signature in voice and speech. Through the present paper, a new method to trace some neurological diseases at the level of phonation will be shown. In this way, the detection and grading of the neurological disease could be based on a simple voice test. This methodology is benefiting from the advances achieved during the last years in detecting and grading organic pathologies in phonation. The paper hypothesizes that some of the underlying neurological mechanisms affecting phonation produce observable correlates in vocal fold biomechanics and that these correlates behave differentially in neurological diseases than in organic pathologies. A general description about the main hypotheses involved and their validation by acoustic voice analysis based on biomechanical correlates of the neurological disease is given. The validation is carried out on a balanced database of normal and organic dysphonic patients of both genders. Selected study cases will be presented to illustrate the possibilities offered by this methodology.

Neuromorphic detection of speech dynamics

Speech and voice technologies are experiencing a profound review as new paradigms are sought to overcome some specific problems which cannot be completely solved by classical approaches. Neuromorphic Speech Processing is an emerging area in which research is turning the face to understand the natural neural processing of speech by the Human Auditory System in order to capture the basic mechanisms solving difficult tasks in an efficient way. In the present paper a further step ahead is presented in the approach to mimic basic neural speech processing by simple neuromorphic units standing on previous work to show how formant dynamics - and henceforth consonantal features - can be detected by using a general neuromorphic unit which can mimic the functionality of certain neurons found in the upper auditory pathways. Using these simple building blocks a General Speech Processing Architecture can be synthesized as a layered structure. Results from different simulation stages are provided as well as a discussion on implementation details. Conclusions and future work are oriented to describe the functionality to be covered in the next research steps.

Simulating the phonological auditory cortex from vowel representation spaces to categories

Abstract Vowels are important clues supporting speech perception. Nevertheless in Computational Perception the definition of vowels is a very complex and elusive issue. The purpose of the present paper is to give a possible definition under the perceptual point of view. A vowel could be defined as an assignment of an acoustic–phonetic pattern to a specific categorical representation space. This assignment would be competitively instantiated in the cortical structures, depending on the specific phonological framework of the listeners language. An experimental framework is designed to test this definition on a Neuromorphic Speech Processing Architecture. Results from experiments to test reference patterns in Spanish, and possible extension to other languages with a larger repertoire of categories are presented and discussed.

Monitoring amyotrophic lateral sclerosis by biomechanical modeling of speech production

Neuromotor Degenerative Diseases (NDD) affecting mainly sub-thalamic and extra-pyramidal neuromotor structures leave significant marks in speech and phonation correlates. These may be used in the characterization, detection, grading and monitoring diseases and their progress in a non-invasive way. Considering that speech and phonation recording can be carried out using handy and low-cost instrumentation, speech and phonation correlates may be quite adequate candidates to define specific NDD biomarkers for disease progress monitoring protocols. The purpose of the paper is to present the fundamentals of speech articulation biomechanical modeling from the level of signal processing to neuromotor activity inference. This backward pathway involves several inverse problems, which are addressed separately. Results from study cases relevant in Amyotrophic Lateral Sclerosis are presented and discussed. The conclusions of the research show that several correlates may be reliably established, and that monitoring disease state and progress may rely on some biomechanical correlates informing on jaw and tongue neuromotor residual activity. Possible applications of the methodology to other neurodegenerative diseases are also discussed.

Estimating Tremor in Vocal Fold Biomechanics for Neurological Disease Characterization

Neurological Diseases (ND) are affecting larger segments of aging population every year. Treatment is dependent on expensive accurate and frequent monitoring. It is well known that ND leave correlates in speech and phonation. The present work shows a method to detect alterations in vocal fold tension during phonation. These may appear either as hypertension or as cyclical tremor. Estimations of tremor may be produced by auto-regressive modeling of the vocal fold tension series in sustained phonation. The correlates obtained are a set of cyclicality coefficients, the frequency and the root mean square amplitude of the tremor. Statistical distributions of these correlates obtained from a set of male and female subjects are presented. Results from five study cases of female voice are also given.

Neurological disease detection and monitoring from voice production

The dramatic impact of neurological degenerative pathologies in life quality is a growing concern. It is well known that many neurological diseases leave a fingerprint in voice and speech production. Many techniques have been designed for the detection, diagnose and monitoring the neurological disease. Most of them are costly or difficult to extend to primary attention medical services. Through the present paper it will be shown how some neurological diseases can be traced at the level of phonation. The detection procedure would be based on a simple voice test. The availability of advanced tools and methodologies to monitor the organic pathology of voice would facilitate the implantation of these tests. The paper hypothesizes that some of the underlying mechanisms affecting the production of voice produce measurable correlates in vocal fold biomechanics. A general description of the methodological foundations for the voice analysis system which can estimate correlates to the neurological disease is shown. Some study cases will be presented to illustrate the possibilities of the methodology to monitor neurological diseases by voice.

A Bio-inspired Architecture for Cognitive Audio

A comprehensive view of speech and voice technologies is now demanding better and more complex tools amenable of extracting as much knowledge about sound and speech as possible. Many knowledge-extraction tasks from speech and voice share well-known procedures at the algorithmic level under the point of view of bio-inspiration. The same resources employed to decode speech phones may be used in the characterization of the speaker (gender, age, speaking group, etc.). Based on these facts the present paper examines a hierarchy of sound processing levels at the auditory and perceptual levels on the brain neural paths which can be translated into a bio-inspired audio-processing architecture. Through this paper its fundamental characteristics are analyzed in relation with current tendencies in cognitive audio processing. Examples extracted from speech processing applications in the domain of acoustic-phonetics are presented. These may find applicability in speakers characterization, forensics, and biometry, among others.

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.

Phonation biomechanic analysis of Alzheimer's Disease cases

Speech production in patients suffering of dementias of Alzheimer?s type is known to experience noticeable changes with respect to normative speakers. Classically this kind of speech has been described as presenting altered prosody, rhythmic pace, anomy, or impaired semantics. Phonation, conceived as the production of voice in voiced speech fragments remains as an unexplored field. The aim of the present paper is to open a preliminary study presenting biomechanical estimates from phonation produced by two patients (male and female) suffering Alzheimer?s Disease (AD), contrasted on two controls of both genders (CS: control speakers). A vocal fold biomechanical model is inverted to facilitate estimates of the vocal fold stiffness to analyze significant segments of phonated speech as long vowels and fillers. The estimates of both the AD patients and CS subjects are contrasted on a database of phonation features from a normative speaker population of both genders, as well as in paired tests contrasting AD and CS subjects. Results show the possibility of establishing significant discrimination between AD and CS when using f0, as well as vocal fold body stiffness, although this last feature seems to be more relevant and shows larger statistical significance.