Characterizing intonation deficit in motor speech disorders : an autosegmental-metrical analysis of spontaneous speech in hypokinetic dysarthria, ataxic dysarthria and foreign accent syndrome

The autosegmental-metrical (AM) framework represents an established methodology for intonational analysis in unimpaired speaker populations but has found little application in describing intonation in motor speech disorders (MSDs). This study compared the intonation patterns of unimpaired participants (CON) and those with Parkinson's disease (PD), ataxic dysarthria (AT), and foreign accent syndrome (FAS) to evaluate the approach's potential for distinguishing types of MSDs from each other and from unimpaired speech. Spontaneous speech from 8 PD, 8 AT, 4 FAS, and 10 CON speakers were analyzed in relation to inventory and prevalence of pitch patterns, accentuation, and phrasing. Acoustic-phonetic baseline measures (maximum-phonation-duration, speech rate, and F0-variability) were also performed. Results: The analyses yielded differences between MSD and CON groups and between the clinical groups in regard to prevalence, accentuation, and phrasing. AT and FAS speakers used more rising and high pitch accents than PD and CON speakers. The AT group used the highest number of pitch accents per phrase, and all 3 MSD groups produced significantly shorter phrases than the CON group. The study succeeded in differentiating MSDs on the basis of intonational performances by using the AM approach, thus, demonstrating its potential for charting intonational profiles in clinical populations

Jaw Rotation in Dysarthria Measured With a Single Electromagnetic Articulography Sensor

Purpose This study evaluated a novel method for characterizing jaw rotation using orientation data from a single electromagnetic articulography sensor. This method was optimized for clinical application, and a preliminary examination of clinical feasibility and value was undertaken. Method The computational adequacy of the single-sensor orientation method was evaluated through comparisons of jaw-rotation histories calculated from dual-sensor positional data for 16 typical talkers. The clinical feasibility and potential value of single-sensor jaw rotation were assessed through comparisons of 7 talkers with dysarthria and 19 typical talkers in connected speech. Results The single-sensor orientation method allowed faster and safer participant preparation, required lower data-acquisition costs, and generated less high-frequency artifact than the dual-sensor positional approach. All talkers with dysarthria, regardless of severity, demonstrated jaw-rotation histories with more numerous changes in movement direction and reduced smoothness compared with typical talkers. Conclusions Results suggest that the single-sensor orientation method for calculating jaw rotation during speech is clinically feasible. Given the preliminary nature of this study and the small participant pool, the clinical value of such measures remains an open question. Further work must address the potential confound of reduced speaking rate on movement smoothness

Rhythmic performance in hypokinetic dysarthria : relationship between reading, spontaneous speech and diadochokinetic tasks

Purpose: This study aimed to investigate whether rhythm metrics are sensitive to change in speakers with mild hypokinetic dysarthria, whether such changes can be detected in reading and spontaneous speech, and whether diadochokinetic (DDK) performance relates to rhythmic properties of speech tasks. Method: Ten people with Parkinson’s Disease (PwPD) with mild hypokinetic dysarthria and ten healthy control speakers produced DDK repetitions, a reading passage and a spontaneous monologue. Articulation rate, as well as ten rhythm metrics were applied to the speech data. DDK performance was captured by mean, standard deviation (SD) and coefficient of variation (CoV) of syllable duration. Results: Group differences were apparent across both speech tasks, but mainly in spontaneous speech. The control speakers changed their rhythm performance between the two tasks, whereas the PwPD displayed a more constant behaviour. The correlation analysis of speech and DDK tasks resulted in few meaningful relationships. Conclusions: Rhythm metrics appeared to be sensitive to mild levels of impairment in PwPD. They are thus suitable for use as diagnostic or outcome measures. In addition, we demonstrated that conversational data can be used in the investigation of rhythm. Finally, the value of DDK tasks in predicting the rhythm performance during speech could not be demonstrated successfully

Speech and communication in Parkinson’s disease: a cross-sectional exploratory study in the UK

Objective: To assess associations between cognitive status, intelligibility, acoustics and functional communication in PD. Design: Cross-sectional exploratory study of functional communication, including a within-participants experimental design for listener assessment. Setting: A major academic medical centre in the East of England, UK. Participants: Questionnaire data were assessed for 45 people with Parkinson’s disease (PD), who had self-reported speech or communication difficulties and did not have clinical dementia. Acoustic and listener analyses were conducted on read and conversational speech for 20 people with PD and 20 familiar conversation partner controls without speech, language or cognitive difficulties. Main outcome measures: Functional communication assessed by the Communicative Participation Item Bank (CPIB) and Communicative Effectiveness Survey (CES). Results: People with PD had lower intelligibility than controls for both the read (mean difference 13.7%, p=0.009) and conversational (mean difference 16.2%, p=0.04) sentences. Intensity and pause were statistically significant predictors of intelligibility in read sentences. Listeners were less accurate identifying the intended emotion in the speech of people with PD (14.8% point difference across conditions, p=0.02) and this was associated with worse speaker cognitive status (16.7% point difference, p=0.04). Cognitive status was a significant predictor of functional communication using CPIB (F=8.99, p=0.005, η2 = 0.15) but not CES. Intelligibility in conversation sentences was a statistically significant predictor of CPIB (F=4.96, p=0.04, η2 = 0.19) and CES (F=13.65, p=0.002, η2 = 0.43). Read sentence intelligibility was not a significant predictor of either outcome. Conclusions: Cognitive status was an important predictor of functional communication—the role of intelligibility was modest and limited to conversational and not read speech. Our results highlight the importance of focusing on functional communication as well as physical speech impairment in speech and language therapy (SLT) for PD. Our results could inform future trials of SLT techniques for PD

How does prosodic deficit impact naïve listeners recognition of emotion? An analysis with speakers affected by Parkinson's disease

Abstract This study aimed to understand the impact of the prosodic deficit in Parkinson's disease (PD) on the communicative effectiveness of vocal expression of emotion. Fourteen patients with PD and 13 healthy control subjects (HC) uttered the phrase "non è possible, non ora" ("It is not possible, not now") six times reading different emotional narrations. Three experts evaluated the PD subjects' vocal production in terms of their communicative effectiveness. The PD patients were divided into two groups: PD+ (with residual effectiveness) and PD− (with impaired effectiveness). The vocal productions were administered to 30 naïve listeners. They were requested to label the emotion they recognized and to make judgments about their communicative effectiveness. The PD speakers were perceived as less effective than the HC speakers in conveying emotions (especially fear and anger). The PD− group was the most impaired in the expression of emotion, suggesting that speech disorders impact differently at the same stage of the disease with varying degrees of severity

Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

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

Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech