Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation.

Acoustic features extracted from speech can help with the diagnosis of neurological diseases and monitoring of symptoms over time. Temporal segmentation of audio signals into individual words is an important pre-processing step needed prior to extracting acoustic features. Machine learning techniques could be used to automate speech segmentation via automatic speech recognition (ASR) and sequence to sequence alignment. While state-of-the-art ASR models achieve good performance on healthy speech, their performance significantly drops when evaluated on dysarthric speech. Fine-tuning ASR models on impaired speech can improve performance in dysarthric individuals, but it requires representative clinical data, which is difficult to collect and may raise privacy concerns. This study explores the feasibility of using two augmentation methods to increase ASR performance on dysarthric speech: 1) healthy individuals varying their speaking rate and loudness (as is often used in assessments of pathological speech); 2) synthetic speech with variations in speaking rate and accent (to ensure more diverse vocal representations and fairness). Experimental evaluations showed that fine-tuning a pre-trained ASR model with data from these two sources outperformed a model fine-tuned only on real clinical data and matched the performance of a model fine-tuned on the combination of real clinical data and synthetic speech. When evaluated on held-out acoustic data from 24 individuals with various neurological diseases, the best performing model achieved an average word error rate of 5.7% and a mean correct count accuracy of 94.4%. In segmenting the data into individual words, a mean intersection-over-union of 89.2% was obtained against manual parsing (ground truth). It can be concluded that emulated and synthetic augmentations can significantly reduce the need for real clinical data of dysarthric speech when fine-tuning ASR models and, in turn, for speech segmentation.

Behavioral Management of Respiratory/Phonatory Dysfunction for Dysarthria Associated With Neurodegenerative Disease: A Systematic Review.

PURPOSE: This systematic review represents an update to previous reviews of the literature addressing behavioral management of respiratory/phonatory dysfunction in individuals with dysarthria due to neurodegenerative disease. METHOD: Multiple electronic database searches and hand searches of prominent speech-language pathology journals were conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards. RESULTS: The search yielded 1,525 articles, from which 88 met inclusion criteria and were reviewed by two blinded co-investigators. A large range of therapeutic approaches have been added to the evidence base since the last review, including expiratory muscle strength training, singing, and computer- and device-driven programs, as well as a variety of treatment modalities, including teletherapy. Evidence for treatment in several different population groups-including cerebellar ataxia, myotonic dystrophy, autosomal recessive spastic ataxia of Charlevoix-Saguenay, Huntington's disease, multiple system atrophy, and Lewy body dementia-were added to the current review. Synthesis of evidence quality provided strong evidence in support of only one behavioral intervention: Lee Silverman Voice Treatment Program (LSVT LOUD) in people with Parkinson's disease. No other treatment approach or population included in this review demonstrated more than limited evidence, reflecting that these approaches/populations require urgent further examination. CONCLUSION: Suggestions about where future research efforts could be significantly strengthened and how clinicians can apply research findings to their practice are provided. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24964473.

The Change of Vocal Tract Length in People with Parkinson's Disease.

Hypokinetic dysarthria is one of the early symptoms of Parkinson's disease (PD) and has been proposed for early detection and also for monitoring of the progression of the disease. PD reduces the control of vocal tract muscles such as the tongue and lips and, therefore the length of the active vocal tract is altered. However, the change in the vocal tract length due to the disease has not been investigated. The aim of this study was to determine the difference in the apparent vocal tract length (AVTL) between people with PD and age-matched control healthy people. The phoneme, /a/ from the UCI Parkinson's Disease Classification Dataset and the Italian Parkinson's Voice and Speech Dataset were used and AVTL was calculated based on the first four formants of the sustained phoneme (F1-F4). The results show a correlation between Parkinson's disease and an increase in vocal tract length. The most sensitive feature was the AVTL calculated using the first formants of sustained phonemes (F1). The other significant finding reported in this article is that the difference is significant and only appeared in the male participants. However, the size of the database is not sufficiently large to identify the possible confounding factors such as the severity and duration of the disease, medication, age, and comorbidity factors.Clinical relevance-The outcomes of this research have the potential to improve the identification of early Parkinsonian dysarthria and monitor PD progression.

A preliminary study on self-care telemonitoring of dysarthria in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a rare neuromuscular disease which may cause impairments in oro-facial musculature. Most of the individuals with SMA present bulbar signs such as flaccid dysarthria which mines their abilities to speak and, as consequence, their psychic balance. To support clinicians, recent work has demonstrated the feasibility of video-based techniques for assessing the oro-facial functions in patients with neurological disorders such as amyotrophic lateral sclerosis. However, no work has so far focused on automatic and quantitative monitoring of dysarthria in SMA. To overcome limitations this work's aim is to propose a cloud-based store-and-forward telemonitoring system for automatic and quantitative evaluation of oro-facial muscles in individuals with SMA. The system integrates a convolutional neural network (CNN) aimed at identifying the position of facial landmarks from video recordings acquired via a web application by an SMA patient.Clinical relevance- The proposed work is in the preliminary stage, but it represents the first step towards a better understanding of the bulbar-functions' evolution in patients with SMA.

Evaluation of orofacial force-related measures using a novel measuring device: explanation of associations with speech rate in dysarthria.

BACKGROUND: The aim of this study was to examine the potential associations between orofacial force-related measures and speech rate in matched groups of 23 adults with dysarthria, and 69 healthy adults. RESEARCH DESIGN AND METHODS: A novel piezoresistive sensor-based device was utilized to obtain the orofacial maximum forces (OMFs) and rate of force development (RFD) measures. The study computed alternating motion rates (AMRs), sequential motion rates (SMRs), and articulation rate (AR) for all participants. The analysis included between-group comparisons and correlation analyses. The study also examined the reliability of the OMFs and RFD measures. RESULTS: Individuals with dysarthria exhibited significantly slower speech rates (approximately 41.89% to 56.53% slower) compared to the control group. Except for a few exceptions in the jaw, the dysarthria group demonstrated significantly lower OMFs and RFD measures. The correlation analysis revealed that OMFs were weakly to moderately correlated (r = .488-.674) and RFD measures were very weak to moderately correlated (r = .047-.578) with speech rate measures. CONCLUSIONS: The findings suggest that reduced OMFs and RFD measures may contribute to the slowed speech rate observed in adults with dysarthria. The study also highlights that OMFs are significantly more reliable (day-to-day) than RFD measures.

Subclinical articulatory changes of vowel parameters in Korean amyotrophic lateral sclerosis patients with perceptually normal voices.

The available quantitative methods for evaluating bulbar dysfunction in patients with amyotrophic lateral sclerosis (ALS) are limited. We aimed to characterize vowel properties in Korean ALS patients, investigate associations between vowel parameters and clinical features of ALS, and analyze subclinical articulatory changes of vowel parameters in those with perceptually normal voices. Forty-three patients with ALS (27 with dysarthria and 16 without dysarthria) and 20 healthy controls were prospectively collected in the study. Dysarthria was assessed using the ALS Functional Rating Scale-Revised (ALSFRS-R) speech subscores, with any loss of 4 points indicating the presence of dysarthria. The structured speech samples were recorded and analyzed using Praat software. For three corner vowels (/a/, /i/, and /u/), data on the vowel duration, fundamental frequency, frequencies of the first two formants (F1 and F2), harmonics-to-noise ratio, vowel space area (VSA), and vowel articulation index (VAI) were extracted from the speech samples. Corner vowel durations were significantly longer in ALS patients with dysarthria than in healthy controls. The F1 frequency of /a/, F2 frequencies of /i/ and /u/, the VSA, and the VAI showed significant differences between ALS patients with dysarthria and healthy controls. The area under the curve (AUC) was 0.912. The F1 frequency of /a/ and the VSA were the major determinants for differentiating ALS patients who had not yet developed apparent dysarthria from healthy controls (AUC 0.887). In linear regression analyses, as the ALSFRS-R speech subscore decreased, both the VSA and VAI were reduced. In contrast, vowel durations were found to be rather prolonged. The analyses of vowel parameters provided a useful metric correlated with disease severity for detecting subclinical bulbar dysfunction in ALS patients.

Utilizing speech analysis to differentiate progressive supranuclear palsy from Parkinson's disease.

INTRODUCTION: Distinguishing Parkinson's disease (PD) from Progressive supranuclear palsy (PSP) at early disease stages is important for clinical trial enrollment and clinical care/prognostication. METHODS: We recruited 21 participants with PSP(n = 11) or PD(n = 10) with reliable caregivers. Standardized passage reading, counting, and sustained phonation were recorded on the BioDigit Home tablet (BioSensics LLC, Newton, MA USA), and speech features from the assessments were analyzed using the BioDigit Speech platform (BioSensics LLC, Newton, MA USA). An independent t-test was performed to compare each speech feature between PSP and PD participants. We also performed Spearman's correlations to evaluate associations between speech measures and clinical scores (e.g., PSP rating scales and MoCA). In addition, the model's performance in classifying PSP and PD was evaluated using Rainbow passage reading analysis. RESULTS: During Rainbow passage reading, PSP participants had a significantly slower articulation rate (2.45(0.49) vs 3.60(0.47) words/minute), lower speech-to-pause ratio (2.33(1.08) vs 3.67(1.18)), intelligibility dynamic time warping (DTW, 0.26(0.19) vs 0.53(0.26)), and similarity DTW (0.43(0.27) vs 0.67(0.13)) compared to PD participants. PSP participants also had longer pause times (17.24(5.47) vs 8.45(3.13) sec) and longer total signal times (52.44(6.67) vs (36.67(6.73) sec) when reading the passage. In terms of the phonation 'a', PSP participants showed a significant higher spectral entropy, spectral centroid, and spectral spread compared to PD participants and no differences were found for phonation 'e'. PD participants had more accurate reverse number counts than PSP participants (14.89(3.86) vs 7.36(4.67)). PSP Rating Scale (PSPRS) dysarthria (r = 0.79, p = 0.004) and bulbar item scores (r = 0.803, p = 0.005) were positively correlated with articulation rate in reverse number counts. Correct reverse number counts were positively correlated with total Montreal Cognitive Assessment scores (r = 0.703, p = 0.016). Machine learning models using passage reading-derived measures obtained an AUC of 0.93, and the sensitivity/specificity in correctly classifying PSP and PD participants were 0.95 and 0.90, respectively. CONCLUSION: Our study demonstrates the feasibility of differentiating PSP from PD using a digital health technology platform. Further multi-center studies are needed to expand and validate our initial findings.

Global Acoustic Speech Temporal Characteristics for Mandarin Speakers With Parkinson's Disease During Syllable Repetition and Passage Reading.

PURPOSE: Previous research has revealed considerable variation in speech rates among English speakers with Parkinson's disease (PD) with slower, faster, or similar rates than controls. The purpose of this study was to characterize speech rates of Mandarin speakers with PD and the corresponding articulation and pause characteristics explaining the speech rates to enhance rate control therapies. METHOD: Eighteen Mandarin speakers with PD and 18 controls produced syllable repetitions and passage reading using their typical speech style. Speech rates, articulation rates, mean pause durations (≥ 10 ms), and the number of pauses with duration between 10 ms and 300 ms and greater than 300 ms were measured and compared between groups and tasks as well as across the initial, middle, and final periods of the passage. Two-way, mixed-model analyses of variance were employed for data analysis. RESULTS: Compared to controls, individuals with PD spoke with similar speech rates and faster articulation rates during passage reading, whereas during syllable repetitions, they produced slower speech rates and comparable articulation rates. The slower syllable repetitions produced by speakers with PD may be explained by longer pauses and more perceptual pauses, whereas faster articulation rates may explain the trend of faster speech rates during reading. Speech and articulation rates accelerated for both groups during passage reading. CONCLUSIONS: Speech rates of Mandarin speakers with PD were characterized by faster articulation, longer pauses, and more perceptual pauses for passage reading. A descriptive model of speech rate suggested that speakers with PD and dysarthria in this study would benefit from rate reduction therapy decreasing articulation rate. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.23982282.

Extracting Phonetic Posterior-Based Features for Detecting Multiple Sclerosis From Speech.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system which, in addition to affecting motor and cognitive functions, may also lead to specific changes in the speech of patients. Speech production, comprehension, repetition and naming tasks, as well as structural and content changes in narratives, might indicate a limitation of executive functions. In this study we present a speech-based machine learning technique to distinguish speakers with relapsing-remitting subtype MS and healthy controls (HC). We exploit the fact that MS might cause a motor speech disorder similar to dysarthria, which, with our hypothesis, might affect the phonetic posterior estimates supplied by a Deep Neural Network acoustic model. From our experimental results, the proposed posterior posteriorgram-based feature extraction approach is useful for detecting MS: depending on the actual speech task, we obtained Equal Error Rate values as low as 13.3%, and AUC scores up to 0.891, indicating a competitive and more consistent classification performance compared to both the x-vector and the openSMILE 'ComParE functionals' attributes. Besides this discrimination performance, the interpretable nature of the phonetic posterior features might also make our method suitable for automatic MS screening or monitoring the progression of the disease. Furthermore, by examining which specific phonetic groups are the most useful for this feature extraction process, the potential utility of the proposed phonetic features could also be utilized in the speech therapy of MS patients.

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis.

BACKGROUND AND OBJECTIVES: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patients' management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. METHODS: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture - called facial landmark Mask RCNN - aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. RESULTS: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. DISCUSSION AND CONCLUSIONS: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria.

Clinical Assessment of Communication-Related Speech Parameters in Dysarthria: The Impact of Perceptual Adaptation.

PURPOSE: In current clinical practice, intelligibility of dysarthric speech is commonly assessed by speech-language therapists (SLTs), in most cases by the therapist caring for the patient being diagnosed. Since SLTs are familiar with dysarthria in general and with the speech of the individual patient to be assessed in particular, they have an adaptation advantage in understanding the patient's utterances. We examined whether and how listeners' assessments of communication-related speech parameters vary as a function of their familiarity with dysarthria in general and with the diagnosed patients in particular. METHOD: Intelligibility, speech naturalness, and perceived listener effort were assessed in 20 persons with dysarthria (PWD). Patients' speech samples were judged by the individual treating therapists, five dysarthria experts who were unfamiliar with the patients, and crowdsourced naïve listeners. Adaptation effects were analyzed using (a) linear mixed models of overall scoring levels, (b) regression models of severity dependence, (c) network analyses of between-listener and between-parameter relationships, and (d) measures of intra- and interobserver consistency. RESULTS: Significant advantages of dysarthria experts over laypeople were found in all parameters. An overall advantage of the treating therapists over nonfamiliar experts was only seen in listening effort. Severity-dependent adaptation effects occurred in all parameters. The therapists' responses were heterogeneous and inconsistent with those of the unfamiliar experts and the naïve listeners. CONCLUSIONS: The way SLTs evaluate communication-relevant speech parameters of the PWD whom they care for is influenced not only by adaptation benefits but also by therapeutic biases. This finding weakens the validity of assessments of communication-relevant speech parameters by the treating therapists themselves and encourages the development and use of alternative methods.

Sensitivity and specificity of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised to detect dysarthria in individuals with amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Given the widespread use of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) to measure disease progression in ALS and recent reports demonstrating its poor sensitivity, we aimed to determine the sensitivity and specificity of the ALSFRS-R bulbar subscale and speech item to detect validated clinical ratings of dysarthria in individuals with ALS. METHODS: Paired ALSFRS-R and validated Speech Intelligibility Test (SIT) data from individuals with ALS were analyzed. Trained raters completed duplicate, independent, and blinded ratings of audio recordings to obtain speech intelligibility (%) and speaking rate (words per minute, WPM). Binary dysarthria profiles were derived (dysarthria ≤96% intelligible and/or <150 WPM). Data were obtained using the Kruskal-Wallis test, receiver-operating characteristic (ROC) curve, area under the curve (AUC), sensitivity and specificity percentages, and positive/negative predictive values (PPV/NPV). RESULTS: A total of 250 paired SIT and ALSFRS-R data points were analyzed. Dysarthria was confirmed in 72.4% (n = 181). Dysarthric speakers demonstrated lower ALSFRS-R bulbar subscale (8.9 vs. 11.2) and speech item (2.7 vs. 3.7) scores (P < .0001). The ALSFRS-R bulbar subscale score had an AUC of 0.81 (95% confidence interval [CI] 0.75 to 0.86). A subscale score of ≤11 yielded a sensitivity of 86%, specificity of 57%, PPV of 84%, and NPV of 60% to correctly identify dysarthria status. The ALSFRS-R speech item score demonstrated an AUC of 0.81 to detect dysarthria (95% CI 0.76 to 0.85), with sensitivity of 79%, specificity of 75%, PPV of 89%, and NPV of 58% for a speech item cutpoint of ≤3. DISCUSSION: The ALSFRS-R bulbar and speech item subscale scores may be useful, inexpensive, and quick tools for monitoring dysarthria status in ALS.

Effects of Aided Communication on Communicative Participation for People With Amyotrophic Lateral Sclerosis.

PURPOSE: Many people with amyotrophic lateral sclerosis (PALS) experience speech changes, which may interfere with participation in communication situations. This study was designed to investigate the effects of aided communication on self-rated communicative participation among PALS and the relationship between speech function and communicative participation for PALS at various stages of speech impairment and communication aid use. METHOD: Participants with amyotrophic lateral sclerosis completed an online questionnaire in which they identified their current communication methods, rated their speech function, and rated their communicative participation in various situations on a modified version of the Communicative Participation Item Bank short form. PALS who reported using aided communication rated their communicative participation under two conditions: with unaided communication only and with access to all of their communication methods. RESULTS: Communication aids appeared to support communicative participation for many participants with dysarthria. Across all levels of speech function, PALS who use aided communication reported better participation under the all-methods condition than the unaided-only condition, with the largest benefits for participants with anarthria (Revised ALS Functional Rating Scale [ALSFRS-R] speech rating = 0). Communicative participation ratings worsened with more severe speech impairment under both conditions for most levels of speech function, but PALS with anarthria (ALSFRS-R speech rating = 0) reported better participation under the all-methods condition than those who used residual speech in combination with non speech methods (ALSFRS-R speech rating = 1). CONCLUSIONS: Aided communication can help PALS continue to participate in various communication situations as their speech function deteriorates. Variability in self-rated communicative participation, even for PALS at the same level of speech function, highlights the need for an individualized approach and consideration of personal and environmental factors in augmentative and alternative communication intervention. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22782986.

Speech Naturalness in the Assessment of Childhood Dysarthria.

PURPOSE: This study investigated perceived speech naturalness estimated by adult listeners in typically developing children and children with dysarthria. We aimed to identify predictors of naturalness among auditory-perceptual parameters and to evaluate the concept of naturalness as a clinical marker of childhood dysarthria. METHOD: In a listening experiment, naive adult listeners rated speech naturalness of 144 typically developing children (3-9 years old) and 28 children with neurological conditions (5-9 years old) on a visual analog scale. Speech samples were recorded using the materials of the Bogenhausen Dysarthria Scales-Childhood Dysarthria, which also provides for auditory-perceptual judgments covering all speech subsystems. RESULTS: Children with dysarthria obtained significantly lower naturalness ratings compared to typically developing children. However, there was a substantial age effect observable in the typically developing children; that is, younger typically developing children were also perceived as somewhat unnatural. The ratings of the typically developing children were influenced by the occurrence of developmental speech features; for the children with neurological conditions, specific symptoms of dysarthria had an additional effect. In both groups, the perception of naturalness was predominantly determined by the children's articulation and intelligibility. CONCLUSIONS: Both symptoms of childhood dysarthria and developmental speech features (e.g., regarding articulation and intelligibility) were associated to some extent with unnatural speech by the listeners. Thus, perceived speech naturalness appears less suitable as a marker of dysarthria in children than in adults.

Speech Motor Profiles in Primary Progressive Aphasia.

PURPOSE: Previous research on motor speech disorders (MSDs) in primary progressive aphasia (PPA) has largely focused on patients with the nonfluent/agrammatic variant of PPA (nfvPPA), with few systematic descriptions of MSDs in variants other than nfvPPA. There has also been an emphasis on studying apraxia of speech, whereas less is known about dysarthria or other forms of MSDs. This study aimed to examine the qualitative and quantitative characteristics of MSDs in a prospective sample of individuals with PPA independent of subtype. METHOD: We included 38 participants with a root diagnosis of PPA according to current consensus criteria, including one case with primary progressive apraxia of speech. Speech tasks comprised various speech modalities and levels of complexity. Expert raters used a novel protocol for auditory speech analyses covering all major dimensions of speech. RESULTS: Of the participants, 47.4% presented with some form of MSD. Individual speech motor profiles varied widely with respect to the different speech dimensions. Besides apraxia of speech, we observed different dysarthria syndromes, special forms of MSDs (e.g., neurogenic stuttering), and mixed forms. Degrees of severity ranged from mild to severe. We also observed MSDs in patients whose speech and language profiles were incompatible with nfvPPA. CONCLUSIONS: The results confirm that MSDs are common in PPA and can manifest in different syndromes. The findings emphasize that future studies of MSDs in PPA should be extended to all clinical variants and should take into account the qualitative characteristics of motor speech dysfunction across speech dimensions. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22555534.

The Relationship Between Acoustic and Kinematic Vowel Space Areas With and Without Normalization for Speakers With and Without Dysarthria.

PURPOSE: Few studies have reported on the vowel space area (VSA) in both acoustic and kinematic domains. This study examined acoustic and kinematic VSAs for speakers with and without dysarthria and evaluated effects of normalization on acoustic and kinematic VSAs and the relationship between these measures. METHOD: Vowel data from 12 speakers with and without dysarthria, presenting with a range of speech abilities, were examined. The speakers included four speakers with Parkinson's disease (PD), four speakers with brain injury (BI), and four neurotypical (NT) speakers. Speech acoustic and kinematic data were acquired simultaneously using electromagnetic articulography during a passage reading task. Raw and normalized VSAs calculated from corner vowels /i/, /æ/, /ɑ/, and /u/ were evaluated. Normalization was achieved through z score transformations to the acoustic and kinematic data. The effect of normalization on variability within and across groups was evaluated. Regression analysis was used across speakers to assess the association between acoustic and kinematic VSAs for both raw and normalized data. RESULTS: When evaluating the speakers as three different groups (i.e., PD, BI, and NT), normalization reduced the standard deviations within each group and changed the relative differences in average magnitude between groups. Regression analysis revealed a significant relationship between normalized, but not raw, acoustic and kinematic VSAs, after the exclusion of an outlier speaker. CONCLUSIONS: Normalization reduces the variability across speakers, within groups, and changes average magnitudes affecting speaker group comparisons. Normalization also influences the correlation between acoustic and kinematic measures. Further investigation of the impact of normalization techniques upon acoustic and kinematic measures is warranted. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22669747.

Dysarthria detection based on a deep learning model with a clinically-interpretable layer.

Studies have shown deep neural networks (DNN) as a potential tool for classifying dysarthric speakers and controls. However, representations used to train DNNs are largely not clinically interpretable, which limits clinical value. Here, a model with a bottleneck layer is trained to jointly learn a classification label and four clinically-interpretable features. Evaluation of two dysarthria subtypes shows that the proposed method can flexibly trade-off between improved classification accuracy and discovery of clinically-interpretable deficit patterns. The analysis using Shapley additive explanation shows the model learns a representation consistent with the disturbances that define the two dysarthria subtypes considered in this work.

Rate Modulation Abilities in Acquired Motor Speech Disorders.

PURPOSE: The purpose of this study was to describe, compare, and understand speech modulation capabilities of patients with varying motor speech disorders (MSDs) in a paradigm in which patients made highly cued attempts to speak faster or slower. METHOD: Twenty-nine patients, 12 with apraxia of speech (AOS; four phonetic and eight prosodic subtype), eight with dysarthria (six hypokinetic and two spastic subtype), and nine patients without any neurogenic MSD completed a standard motor speech evaluation where they were asked to repeat words and sentences, which served as their "natural" speaking rate. They were then asked to repeat lower complexity (counting 1-5; repeating "cat" and "catnip" 3 times each) and higher complexity stimuli (repeating "catastrophe" and "stethoscope" 3 times each and "My physician wrote out a prescription" once) as fast/slow as possible. Word durations and interword intervals were measured. Linear mixed-effects models were used to assess differences related to MSD subtype and stimuli complexity on bidirectional rate modulation capacity as indexed by word duration and interword interval. Articulatory accuracy was also judged and compared. RESULTS: Patients with prosodic AOS demonstrated a reduced ability to go faster; while they performed similarly to patients with spastic dysarthria when counting, patients with spastic dysarthria were able to increase rate similar to controls during sentence repetition; patients with prosodic AOS could not and made increased articulatory errors attempting to increase rate. AOS patients made more articulatory errors relative to other groups, regardless of condition; however, their percentage of errors reduced with an intentionally slowed speaking rate. CONCLUSIONS: The findings suggest comparative rate modulation abilities in conjunction with their impact on articulatory accuracy may support differential diagnosis between healthy and abnormal speech and among subtypes of MSDs (i.e., type of dysarthria or AOS). Findings need to be validated in a larger, more representative cohort encompassing several types of MSDs. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22044632.

[A multiscale feature extraction algorithm for dysarthric speech recognition].

In this paper, we propose a multi-scale mel domain feature map extraction algorithm to solve the problem that the speech recognition rate of dysarthria is difficult to improve. We used the empirical mode decomposition method to decompose speech signals and extracted Fbank features and their first-order differences for each of the three effective components to construct a new feature map, which could capture details in the frequency domain. Secondly, due to the problems of effective feature loss and high computational complexity in the training process of single channel neural network, we proposed a speech recognition network model in this paper. Finally, training and decoding were performed on the public UA-Speech dataset. The experimental results showed that the accuracy of the speech recognition model of this method reached 92.77%. Therefore, the algorithm proposed in this paper can effectively improve the speech recognition rate of dysarthria.