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

Optimizing Vowel Formant Measurements in Four Acoustic Analysis Systems for Diverse Speaker Groups

PURPOSE

This study systematically assessed the effects of select linear predictive coding (LPC) analysis parameter manipulations on vowel formant measurements for diverse speaker groups using 4 trademarked Speech Acoustic Analysis Software Packages (SAASPs): CSL, Praat, TF32, and WaveSurfer.

METHOD

Productions of 4 words containing the corner vowels were recorded from 4 speaker groups with typical development (male and female adults and male and female children) and 4 speaker groups with Down syndrome (male and female adults and male and female children). Formant frequencies were determined from manual measurements using a consensus analysis procedure to establish formant reference values, and from the 4 SAASPs (using both the default analysis parameters and with adjustments or manipulations to select parameters). Smaller differences between values obtained from the SAASPs and the consensus analysis implied more optimal analysis parameter settings.

RESULTS

Manipulations of default analysis parameters in CSL, Praat, and TF32 yielded more accurate formant measurements, though the benefit was not uniform across speaker groups and formants. In WaveSurfer, manipulations did not improve formant measurements.

CONCLUSIONS

The effects of analysis parameter manipulations on accuracy of formant-frequency measurements varied by SAASP, speaker group, and formant. The information from this study helps to guide clinical and research applications of SAASPs.

Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson's disease

BACKGROUND AND OBJECTIVE

Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinson's disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility.

METHODS

This paper deals with quantitative prosodic analysis of neutral, stress-modified and rhymed speech in patients with PD. The analysis of prosody is based on quantification of monopitch, monoloudness, and speech rate abnormalities. Experimental dataset consists of 98 patients with PD and 51 healthy speakers. For the purpose of HD identification, sequential floating feature selection algorithm and random forests classifier is used. In this paper, we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech.

RESULTS

Prosodic features obtained from stress-modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody. Features calculated from poem recitation task outperformed both reading tasks in the case of gender-undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD. Additionally, some gender-related patterns of dysprosody has been observed.

CONCLUSIONS

This paper confirms reduced variation of fundamental frequency in PD patients with HD. Interestingly, increased variability of speech intensity compared to healthy speakers has been detected. Regarding speech rate disturbances, our results does not report any particular pattern. We conclude further development of prosodic features quantifying the relationship between monopitch, monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis.

Deep brain stimulation exacerbates hypokinetic dysarthria in a rat model of Parkinson's disease

Motor symptoms of Parkinson's disease (PD) follow the degeneration of dopaminergic neurons in the substantia nigra pars compacta. Deep brain stimulation (DBS) treats some parkinsonian symptoms, such as tremor, rigidity, and bradykinesia, but may worsen certain medial motor symptoms, including hypokinetic dysarthria. The mechanisms by which DBS exacerbates dysarthria while improving other symptoms are unclear and difficult to study in human patients. This study proposes an animal model of DBS-exacerbated dysarthria. We use the unilateral, 6-hydroxydopamine (6-OHDA) rat model of PD to test the hypothesis that DBS exacerbates quantifiable aspects of vocalization. Mating calls were recorded from sexually experienced male rats under healthy and parkinsonian conditions and during DBS of the subthalamic nucleus. Relative to healthy rats, parkinsonian animals made fewer calls with shorter and less complex vocalizations. In the parkinsonian rats, putatively therapeutic DBS further reduced call frequency, duration, and complexity. The individual utterances of parkinsonian rats spanned a greater bandwidth than those of healthy rats, potentially reducing the effectiveness of the vocal signal. This utterance bandwidth was further increased by DBS. We propose that the parkinsonism-associated changes in call frequency, duration, complexity, and dynamic range combine to constitute a rat analog of parkinsonian dysarthria. Because DBS exacerbates the parkinsonism-associated changes in each of these metrics, the subthalamic stimulated 6-OHDA rat is a good model of DBS-induced hypokinetic dysarthria in PD. This model will help researchers examine how DBS alleviates many motor symptoms of PD while exacerbating parkinsonian speech deficits that can greatly diminish patient quality of life.

Automatic detection of Parkinson's disease in running speech spoken in three different languages

The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.

Quantitative assessment of motor speech abnormalities in idiopathic rapid eye movement sleep behaviour disorder

OBJECTIVE

Patients with idiopathic rapid eye movement sleep behaviour disorder (RBD) are at substantial risk for developing Parkinson's disease (PD) or related neurodegenerative disorders. Speech is an important indicator of motor function and movement coordination, and therefore may be an extremely sensitive early marker of changes due to prodromal neurodegeneration.

METHODS

Speech data were acquired from 16 RBD subjects and 16 age- and sex-matched healthy control subjects. Objective acoustic assessment of 15 speech dimensions representing various phonatory, articulatory, and prosodic deviations was performed. Statistical models were applied to characterise speech disorders in RBD and to estimate sensitivity and specificity in differentiating between RBD and control subjects.

RESULTS

Some form of speech impairment was revealed in 88% of RBD subjects. Articulatory deficits were the most prominent findings in RBD. In comparison to controls, the RBD group showed significant alterations in irregular alternating motion rates (p = 0.009) and articulatory decay (p = 0.01). The combination of four distinctive speech dimensions, including aperiodicity, irregular alternating motion rates, articulatory decay, and dysfluency, led to 96% sensitivity and 79% specificity in discriminating between RBD and control subjects. Speech impairment was significantly more pronounced in RBD subjects with the motor score of the Unified Parkinson's Disease Rating Scale greater than 4 points when compared to other RBD individuals.

CONCLUSION

Simple quantitative speech motor measures may be suitable for the reliable detection of prodromal neurodegeneration in subjects with RBD, and therefore may provide important outcomes for future therapy trials.

Automatic Evaluation of Speech Rhythm Instability and Acceleration in Dysarthrias Associated with Basal Ganglia Dysfunction

Speech rhythm abnormalities are commonly present in patients with different neurodegenerative disorders. These alterations are hypothesized to be a consequence of disruption to the basal ganglia circuitry involving dysfunction of motor planning, programing, and execution, which can be detected by a syllable repetition paradigm. Therefore, the aim of the present study was to design a robust signal processing technique that allows the automatic detection of spectrally distinctive nuclei of syllable vocalizations and to determine speech features that represent rhythm instability (RI) and rhythm acceleration (RA). A further aim was to elucidate specific patterns of dysrhythmia across various neurodegenerative disorders that share disruption of basal ganglia function. Speech samples based on repetition of the syllable /pa/ at a self-determined steady pace were acquired from 109 subjects, including 22 with Parkinson's disease (PD), 11 progressive supranuclear palsy (PSP), 9 multiple system atrophy (MSA), 24 ephedrone-induced parkinsonism (EP), 20 Huntington's disease (HD), and 23 healthy controls. Subsequently, an algorithm for the automatic detection of syllables as well as features representing RI and RA were designed. The proposed detection algorithm was able to correctly identify syllables and remove erroneous detections due to excessive inspiration and non-speech sounds with a very high accuracy of 99.6%. Instability of vocal pace performance was observed in PSP, MSA, EP, and HD groups. Significantly increased pace acceleration was observed only in the PD group. Although not significant, a tendency for pace acceleration was observed also in the PSP and MSA groups. Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences. We envisage the current approach to become the first step toward the development of acoustic technologies allowing automated assessment of rhythm in dysarthrias.

Multiple factors are involved in the dysarthria associated with Parkinson's disease: a review with implications for clinical practice and research

PURPOSE

Motor speech abnormalities are highly common and debilitating in individuals with idiopathic Parkinson's disease (IPD). These abnormalities, collectively termed hypokinetic dysarthria (HKD), have been traditionally attributed to hypokinesia and bradykinesia secondary to muscle rigidity and dopamine deficits. However, the role of rigidity and dopamine in the development of HKD is far from clear. The purpose of the present study was to offer an alternative view of the factors underlying HKD.

METHOD

The authors conducted an extensive, but not exhaustive, review of the literature to examine the evidence for the traditional view versus the alternative view.

RESULTS

The review suggests that HKD is a highly complex and variable phenomenon including multiple factors, such as scaling and maintaining movement amplitude and effort; preplanning and initiation of movements; internal cueing; sensory and temporal processing; automaticity; emotive vocalization; and attention to action (vocal vigilance). Although not part of the dysarthria, nonmotor factors, such as depression, aging, and cognitive-linguistic abnormalities, are likely to contribute to the overall speech symptomatology associated with IPD.

CONCLUSION

These findings have important implications for clinical practice and research.

Characteristics and occurrence of speech impairment in Huntington's disease: possible influence of antipsychotic medication

Although motor speech impairment is a common manifestation of Huntington's disease (HD), its description remains limited. The aim of the current study was therefore to estimate the occurrence and characteristics of speech disorder in HD and to explore the influence of antipsychotic medication on speech performance. Speech samples, including reading passage and monologue, were acquired from 40 individuals diagnosed with HD and 40 age- and sex-matched healthy controls. Objective acoustic analyses were used to evaluate key aspects of speech including vowel articulation, intensity, pitch and timing. A predictive model was constructed to detect the occurrence and most prominent patterns of speech dysfunction in HD. We revealed that 93% of HD patients manifest some degree of speech impairment. Decreased number of pauses, slower articulation rate, imprecise vowel articulation and excess intensity variations were found to be the most salient patterns of speech dysfunction in HD. We further demonstrated that antipsychotic medication may induce excessive loudness and pitch variations perceptually resembling excess patterns of word stress, and may also accentuate general problems with speech timing. Additionally, antipsychotics induced a slight improvement of vowel articulation. Specific speech alterations observed in HD patients indicate that speech production may reflect the pathophysiology of the disease as well as treatment effects, and may therefore be considered a valuable marker of functional disability in HD.

Automatic assessment of vowel space area

Vowel space area (VSA) is an attractive metric for the study of speech production deficits and reductions in intelligibility, in addition to the traditional study of vowel distinctiveness. Traditional VSA estimates are not currently sufficiently sensitive to map to production deficits. The present report describes an automated algorithm using healthy, connected speech rather than single syllables and estimates the entire vowel working space rather than corner vowels. Analyses reveal a strong correlation between the traditional VSA and automated estimates. When the two methods diverge, the automated method seems to provide a more accurate area since it accounts for all vowels.