Roman Cmejla

Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease

An assessment of vocal impairment is presented for separating healthy people from persons with early untreated Parkinsons disease (PD). This studys main purpose was to (a) determine whether voice and speech disorder are present from early stages of PD before starting dopaminergic pharmacotherapy, (b) ascertain the specific characteristics of the PD-related vocal impairment, (c) identify PD-related acoustic signatures for the major part of traditional clinically used measurement methods with respect to their automatic assessment, and (d) design new automatic measurement methods of articulation. The varied speech data were collected from 46 Czech native speakers, 23 with PD. Subsequently, 19 representative measurements were pre-selected, and Wald sequential analysis was then applied to assess the efficiency of each measure and the extent of vocal impairment of each subject. It was found that measurement of the fundamental frequency variations applied to two selected tasks was the best method for separating healthy from PD subjects. On the basis of objective acoustic measures, statistical decision-making theory, and validation from practicing speech therapists, it has been demonstrated that 78% of early untreated PD subjects indicate some form of vocal impairment. The speech defects thus uncovered differ individually in various characteristics including phonation, articulation, and prosody.

Epileptic high-frequency network activity in a model of non-lesional temporal lobe epilepsy

High-frequency cortical activity, particularly in the 250–600 Hz (fast ripple) band, has been implicated in playing a crucial role in epileptogenesis and seizure generation. Fast ripples are highly specific for the seizure initiation zone. However, evidence for the association of fast ripples with epileptic foci depends on animal models and human cases with substantial lesions in the form of hippocampal sclerosis, which suggests that neuronal loss may be required for fast ripples. In the present work, we tested whether cell loss is a necessary prerequisite for the generation of fast ripples, using a non-lesional model of temporal lobe epilepsy that lacks hippocampal sclerosis. The model is induced by unilateral intrahippocampal injection of tetanus toxin. Recordings from the hippocampi of freely-moving epileptic rats revealed high-frequency activity (>100 Hz), including fast ripples. High-frequency activity was present both during interictal discharges and seizure onset. Interictal fast ripples proved a significantly more reliable marker of the primary epileptogenic zone than the presence of either interictal discharges or ripples (100–250 Hz). These results suggest that fast ripple activity should be considered for its potential value in the pre-surgical workup of non-lesional temporal lobe epilepsy.

Imprecise vowel articulation as a potential early marker of Parkinson's disease: effect of speaking task.

The purpose of this study was to analyze vowel articulation across various speaking tasks in a group of 20 early Parkinsons disease (PD) individuals prior to pharmacotherapy. Vowels were extracted from sustained phonation, sentence repetition, reading passage, and monologue. Acoustic analysis was based upon measures of the first (F1) and second (F2) formant of the vowels /a/, /i/, and /u/, vowel space area (VSA), F2i/F2u and vowel articulation index (VAI). Parkinsonian speakers manifested abnormalities in vowel articulation across F2u, VSA, F2i/F2u, and VAI in all speaking tasks except sustained phonation, compared to 15 age-matched healthy control participants. Findings suggest that sustained phonation is an inappropriate task to investigate vowel articulation in early PD. In contrast, monologue was the most sensitive in differentiating between controls and PD patients, with classification accuracy up to 80%. Measurements of vowel articulation were able to capture even minor abnormalities in speech of PD patients with no perceptible dysarthria. In conclusion, impaired vowel articulation may be considered as a possible early marker of PD. A certain type of speaking task can exert significant influence on vowel articulation. Specifically, complex tasks such as monologue are more likely to elicit articulatory deficits in parkinsonian speech, compared to other speaking tasks.

Automatic evaluation of articulatory disorders in Parkinson's disease

Although articulatory deficits represent an important manifestation of dysarthria in Parkinsons disease (PD), the most widely used methods currently available for the automatic evaluation of speech performance are focused on the assessment of dysphonia. The aim of the present study was to design a reliable automatic approach for the precise estimation of articulatory deficits in PD. Twenty-four individuals diagnosed with de novo PD and twenty-two age-matched healthy controls were recruited. Each participant performed diadochokinetic tasks based upon the fast repetition of /pa/-/ta/-/ka/ syllables. All phonemes were manually labeled and an algorithm for their automatic detection was designed. Subsequently, 13 features describing six different articulatory aspects of speech including vowel quality, coordination of laryngeal and supralaryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing were analyzed. In addition, a classification experiment using a support vector machine based on articulatory features was proposed to differentiate between PD patients and healthy controls. The proposed detection algorithm reached approximately 80% accuracy for a 5 ms threshold of absolute difference between manually labeled references and automatically detected positions. When compared to controls, PD patients showed impaired articulatory performance in all investigated speech dimensions ( ). Moreover, using the six features representing different aspects of articulation, the best overall classification result attained a success rate of 88% in separating PD from controls. Imprecise consonant articulation was found to be the most powerful indicator of PD-related dysarthria. We envisage our approach as the first step towards development of acoustic methods allowing the automated assessment of articulatory features in dysarthrias.

Acoustic assessment of voice and speech disorders in Parkinson's disease through quick vocal test.

The disorders of voice and speech in Parkinson’s disease (PD) result from involvements in several subsystems including respiration, phonation, articulation, and prosody. We investigated the feasibility of acoustic measures for the identification of voice and speech disorders in PD, using a quick vocal test consisting of sustained phonation, diadochokinetic task, and running speech. Various traditional and novel acoustic measurements have been designed in order to be gender independent, represent all speech subsystems, reduce the time required for voice investigation, and provide a reliable automated assessment in practice.

Automated analysis of connected speech reveals early biomarkers of Parkinson’s disease in patients with rapid eye movement sleep behaviour disorder

For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson’s disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.

Detection of Interictal Epileptiform Discharges Using Signal Envelope Distribution Modelling: Application to Epileptic and Non-Epileptic Intracranial Recordings

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector’s specificity and having important implications for future development of spike detectors in general.

Acoustic investigation of stress patterns in Parkinson's disease.

OBJECTIVES Although reduced stress is thought to be one of the most deviant speech dimensions in hypokinetic dysarthria associated with Parkinsons disease (PD), the mechanisms of stress production in PD have not been thoroughly explored by objective methods. The aim of the present study was to quantify the effect of PD on prosodic characteristics and to describe contrastive stress patterns in parkinsonian speech. METHODS The ability of 20 male speakers with early PD and 16 age- and gender-matched healthy controls (HCs) to signal contrastive stress was investigated. Each participant was instructed to unnaturally emphasize five key words while reading a short block of text. Acoustic analyses were based on the measurement of pitch, intensity, and duration. In addition, an innovative measurement termed the stress pattern index (SPI) was designed to mirror the effect of all distinct acoustic cues exploited during stress production. RESULTS Although PD patients demonstrated a reduced ability to convey contrastive stress, they could still notably increase pitch, intensity, and duration to emphasize a word within a sentence. No differences were revealed between PD and HC stress productions using the measurements of pitch, intensity, duration, and intensity range. However, restricted SPI and pitch range were evident in the PD group. CONCLUSIONS A reduced ability to express stress seems to be the distinctive pattern of hypokinetic dysarthria, even in the early stages of PD. Because PD patients were able to consciously improve their speech performance using multiple acoustic cues, the introduction of speech therapy may be rewarding.

Objective Acoustic Quantification of Phonatory Dysfunction in Huntington's Disease

Purpose Although speech motor changes are reported as a common sign of Huntington’s disease (HD), the most prominent signs of voice dysfunction remain unknown. The aim of the current study was to explore specific changes in phonatory function in subjects with HD. Method 34 subjects with HD and 34 age- and sex-matched healthy controls were examined. Participants performed sustained vowel phonation for subsequent analyses of airflow insufficiency, aperiodicity, irregular vibrations of vocal folds, signal perturbations, increased noise, and articulation deficiency. In total, 272 phonations were collected and 12 voice parameters were extracted. Subsequently, a predictive model was built to find the most salient patterns of voice disorders in HD. The results were also correlated with disease severity according to the Unified HD Rating Scale (UHDRS) motor score. Results Subjects with HD showed deterioration in all investigated phonatory functions. Irregular pitch fluctuations, sudden phonation interruption, increased noise, and misplacement of articulators were found to be most significant patterns of phonatory dysfunction in HD (p<0.001). The combination of these four dysphonia aspects contributed to the best classification performance of 94.1% (sensitivity: 95.1%; specificity: 93.2%) in the separation of HD patients from healthy participants. Our results further indicated stronger associations between sudden phonation interruption and voluntary components of the UHDRS (r = −0.48, p<0.01) and between misplacement of articulators and involuntary components of the UHDRS (r = 0.52, p<0.01). Conclusions Our configuration of phonatory features can detect subtle voice abnormalities in subjects with HD. As impairment of phonatory function in HD was found to parallel increasing motor involvement, a qualitative description of voice dysfunction may be helpful to gain better insight into the pathophysiology of the vocal mechanism.

Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders

The accurate changepoint detection of different signal segments is a frequent challenge in a wide range of applications. With regard to speech utterances, the changepoints are related to significant spectral changes, mostly represented by the borders between two phonemes. The main aim of this study is to design a novel Bayesian autoregressive changepoint detector (BACD) and test its feasibility in the evaluation of fluency and articulatory disorders. The originality of the proposed method consists in its normalizing of a posteriori probability using Bayesian evidence and designing a recursive algorithm for reliable practice. For further evaluation of the BACD, we used data from (a) 118 people with various severity of stuttering to assess the extent of speech disfluency using a short reading passage, and (b) 24 patients with early Parkinsons disease and 22 healthy speakers for evaluation of articulation accuracy using fast syllable repetition. Subsequently, we designed two measures for each type of disorder. While speech disfluency has been related to greater distances between spectral changes, inaccurate dysarthric articulation has instead been associated with lower spectral changes. These findings have been confirmed by statistically significant differences, which were achieved in separating several degrees of disfluency and distinguishing healthy from parkinsonian speakers. In addition, a significant correlation was found between the automatic assessment of speech fluency and the judgment of human experts. In conclusion, the method proposed provides a cost-effective, easily applicable and freely available evaluation of speech disorders, as well as other areas requiring reliable techniques for changepoint detection. In a more modest scope, BACD may be used in diagnosis of disease severity, monitoring treatment, and support for therapist evaluation.