The Evolution of Language

A flurry of theoretical and empirical work concerning the production of and response to facial and vocal expressions has occurred in the past decade. That emotional expressions express emotions is a tautology but may not be a fact. Debates have centered on universality, the nature of emotion, and the link between emotions and expressions. Modern evolutionary theory is informing more models, emphasizing that expressions are directed at a receiver, that the interests of sender and receiver can conflict, that there are many determinants of sending an expression in addition to emotion, that expressions influence the receiver in a variety of ways, and that the receiver's response is more than simply decoding a message.

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Facial and vocal expressions of emotion.

Objectives: Epidemiologic studies of the prevalence and risk factors of voice disorders in the general adult population are rare. The purpose of this investigation was to 1) determine the prevalence of voice disorders, 2) identify variables associated with increased risk of voice disorders, and 3) establish the functional impact of voice disorders on the general population.

Study Design: Cross-sectional telephone survey.

Methods: A random sample (n = 1,326) of adults in Iowa and Utah was interviewed using a questionnaire that addressed three areas related to voice disorders: prevalence, potential risk factors, and occupational consequences/effects.

Results: The lifetime prevalence of a voice disorder was 29.9%, with 6.6% of participants reporting a current voice disorder. Stepwise logistic regression identified specific factors that uniquely contributed to increased odds of reporting a chronic voice disorder including sex (women), age (40–59 years), voice use patterns and demands, esophageal reflux, chemical exposures, and frequent cold/sinus infections. However, tobacco or alcohol use did not independently increase the odds of reporting of a chronic voice disorder. Voice disorders adversely impacted job performance and attendance, with 4.3% of participants indicating that their voice had limited or rendered them unable to do certain tasks in their current job. Furthermore, 7.2% of employed respondents reported that they were absent from work 1 or more days in the past year because of their voice, and 2% reported more than 4 days of voice-related absence.

Conclusions: The results of this large epidemiologic study provide valuable information regarding the prevalence of voice disorders, factors that contribute to voice disorder vulnerability, and the functional impact of voice problems on the general population.

Voice Disorders in the General Population: Prevalence, Risk Factors, and Occupational Impact

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

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Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

Tracking Parkinson's disease (PD) symptom progression often uses the unified Parkinson's disease rating scale (UPDRS) that requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians' estimates), using only simple, self-administered, and noninvasive speech tests. We characterize speech with signal processing algorithms, extracting clinically useful features of average PD progression. Subsequently, we select the most parsimonious model with a robust feature selection algorithm, and statistically map the selected subset of features to UPDRS using linear and nonlinear regression techniques that include classical least squares and nonparametric classification and regression trees. We verify our findings on the largest database of PD speech in existence (~6000 recordings from 42 PD patients, recruited to a six-month, multicenter trial). These findings support the feasibility of frequent, remote, and accurate UPDRS tracking. This technology could play a key part in telemonitoring frameworks that enable large-scale clinical trials into novel PD treatments.

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Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests

Basic Anatomy of the Larynx. Biomechanics of Laryngeal Tissue. Fluid Flow in Respiratory Airways (Breathing). Vocal Fold Oscillation. Generation and Propagation of Sound. The Source-Filter Theory of Vowels. Voice Classification and Life Span Changes. Control of Fundamental Frequency. Control of Vocal Intensity and Efficiency. Vocal Registers. Fluctuations and Perturbations in Vocal Output. Voice Disorders.

Daniel W. Martin

Papers

Principles of voice production