Models and Analysis of Vocal Emissions for Biomedical Applications

The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Metabolic Mechanisms of Vocal Fatigue

Vocal fatigue is among the most debilitating conditions affecting individuals with voice disorders. Impressions about mechanisms potentially underlying vocal fatigue have varied depending on how fatigue is defined, participants studied, and measures made, thereby impacting the selection of treatment strategies that may alleviate the condition. However, little is currently known about actual metabolic mechanisms of vocal fatigue. The current study aimed to address this issue by investigating the hypothesis that neuromuscular inefficiency, cardiovascular recovery deficits, or both, may play a role in fatigue. The approach replicated well-vetted approaches in exercise physiology. Metabolic profiles of subjects with vocal fatigue were assessed using gas exchange measures in comparison to two non-fatigue groups: vocally healthy and cardiovascular trained individuals, recruited based on results from a newly vetted questionnaire, the Vocal Fatigue Index (VFI) and laryngeal examination. Participants read out loud at two different loudness levels for a duration of 5 minutes for each task with periods of rest between tasks. Metabolic cost for and recovery time from reading were same across all groups. Oxygen uptake and recovery kinetics (EPOC), ratings of perceived exertion revealed interesting patterns in individuals with vocal fatigue compared to cardiovascular trained individuals in particular. Specifically, slow oxygen uptake kinetics in the vocal fatigue compared to the cardiovascular trained group pointed to utilization of anaerobic energy source to meet the demands of the reading task in the vocal fatigue group, suggesting neuromuscular inefficiency. In contrast, rapid oxygen uptake kinetics in the cardiovascular trained group pointed to utilization of aerobic energy sources and greater neuromuscular efficiency. Similarly, a greater number of individuals in vocal fatigue and vocally healthy groups showed an increase in oxygen consumption post reading (EPOC) compared to the cardiovascular trained group, indicating possible cardiovascular recovery deficits in the former groups. In addition to uncovering potential mechanisms underlying vocal fatigue, including neuromuscular inefficiency and cardiovascular recovery deficits, results from the present study highlight the potential importance of aerobic training to generate aerobic energy required for vocal task demands for both ease of task performance and recovery from it

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Physiologically-Motivated Feature Extraction Methods for Speaker Recognition

Speaker recognition has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade. However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as identification where enrollment and testing data may not have similar phonetic coverage. This dissertation introduces new features based on vocal source characteristics intended to capture physiological information related to the laryngeal excitation energy of a speaker. These features, including RPCC, GLFCC and TPCC, represent the unique characteristics of speech production not represented in current state-of-the-art speaker identification systems. The proposed features are evaluated through three experimental paradigms including cross-lingual speaker identification, cross song-type avian speaker identification and mono-lingual speaker identification. The experimental results show that the proposed features provide information about speaker characteristics that is significantly different in nature from the phonetically-focused information present in traditional spectral features. The incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks

Advances in Management of Voice and Swallowing Disorders

Special Issue “Advances in Management of Voice and Swallowing Disorders” is dedicated to innovations in screening and assessment and the effectiveness of interventions in both dysphonia and dysphagia. In contemporary practice, novel techniques have been introduced in diagnostics and rehabilitative interventions (e.g., machine learning, electrical stimulation). Similarly, advancements in methodological approaches to validate measures have been introduced (e.g., item response theory using Rasch analysis), prompting the need to develop new, robust measures for use in clinics and intervention studies. Against this backdrop, this Special Issue focuses on studies aiming to improve early diagnostics of laryngological disorders and its management. This issue also welcomes the submission of studies on diagnostic accuracy and psychometrics performance of existing and newly developed measures. This includes but is not limited to studies investigating screening tools with sound diagnostic accuracy and robust psychometric properties. Furthermore, interventions with high levels of evidence in relation to clinical outcome using robust methodology (e.g., sophisticated meta-analytic approaches) are of great interest. This issue provides an overview of the latest advances in voice and swallowing disorders