Feedback From Automatic Speech Recognition to Elicit Clear Speech in Healthy Speakers.

PURPOSE: This study assessed the effectiveness of feedback generated by automatic speech recognition (ASR) for eliciting clear speech from young, healthy individuals. As a preliminary step toward exploring a novel method for eliciting clear speech in patients with dysarthria, we investigated the effects of ASR feedback in healthy controls. If successful, ASR feedback has the potential to facilitate independent, at-home clear speech practice. METHOD: Twenty-three healthy control speakers (ages 23-40 years) read sentences aloud in three speaking modes: Habitual, Clear (over-enunciated), and in response to ASR feedback (ASR). In the ASR condition, we used Mozilla DeepSpeech to transcribe speech samples and provide participants with a value indicating the accuracy of the ASR's transcription. For speakers who achieved sufficiently high ASR accuracy, noise was added to their speech at a participant-specific signal-to-noise ratio to ensure that each participant had to over-enunciate to achieve high ASR accuracy. RESULTS: Compared to habitual speech, speech produced in the ASR and Clear conditions was clearer, as rated by speech-language pathologists, and more intelligible, per speech-language pathologist transcriptions. Speech in the Clear and ASR conditions aligned on several acoustic measures, particularly those associated with increased vowel distinctiveness and decreased speaking rate. However, ASR accuracy, intelligibility, and clarity were each correlated with different speech features, which may have implications for how people change their speech for ASR feedback. CONCLUSIONS: ASR successfully elicited outcomes similar to clear speech in healthy speakers. Future work should investigate its efficacy in eliciting clear speech in people with dysarthria.

Amyotrophic Lateral Sclerosis-Bulbar Dysfunction Index-Remote: Test-Retest and Interrater Reliability of Candidate Items.

PURPOSE: The primary aim of this study was to establish the reliability of candidate items as a step in the development of the Amyotrophic Lateral Sclerosis-Bulbar Dysfunction Index-Remote (ALS-BDI-Remote), a novel tool being developed for the detection and monitoring of bulbar signs and symptoms in remote settings. METHOD: The set of candidate items included 40 items covering three domains: cranial nerve examination, auditory-perceptual evaluation, and functional assessment. Forty-eight participants diagnosed with ALS and exhibiting a range of bulbar disease severity were included. Data collection for each participant took place on Zoom over three sessions. During Session 1, the participants were instructed to adjust their Zoom settings and to optimize their recording environment (e.g., lighting, background noise). Their cognition and eating were screened to determine their ability to follow instructions and their eligibility to perform the swallowing and chewing tasks. During Session 2, two speech-language pathologists (SLPs) administered the tool consecutively to determine the items' interrater reliability. During Session 3, one of the SLPs readministered the tool within 2 weeks of Session 1 to assess test-retest reliability. The reliability of each item was estimated using weighted kappa and the percentage of agreement. To be considered reliable, the items had to reach a threshold of 0.5 weighted kappa or 80% percentage agreement (if skewed distribution of the scores) for both interrater and test-retest reliability. RESULTS: In total, 33 of the 40 candidate items reached the reliability cutoff for both reliability analyses. All assessment domains included reliable items. Items requiring very good visualization of structures or movements were generally less reliable. CONCLUSIONS: This study resulted in the selection of reliable items to be included in the next version of the ALS-BDI-Remote, which will undergo psychometric evaluation (reliability, validity, and responsiveness analyses). Additionally, the results contributed to our understanding of the remote administration of SLP assessments for telehealth applications.

Acoustic and Kinematic Assessment of Motor Speech Impairment in Patients With Suspected Four-Repeat Tauopathies.

PURPOSE: The aim of this study was to use acoustic and kinematic speech measures to characterize type of motor speech impairment-apraxia of speech (AOS) versus dysarthria-in individuals with four-repeat tauopathy (4RT)-associated syndromes, including nonfluent variant primary progressive aphasia (nfvPPA), primary progressive AOS (PPAOS), corticobasal syndrome (CBS), and progressive supranuclear palsy syndrome (PSPs). METHOD: Twenty patient participants were recruited and stratified into two groups: (a) a motor-speech-impaired group of individuals with nfvPPA, PPAOS, CBS, or PSPs and suspected 4RT pathology ("MSI+") and (b) a non-motor-speech-impaired group of individuals with logopenic variant primary progressive aphasia ("MSI-"). Ten healthy, age-matched controls also participated in the study. Participants completed a battery of speech tasks, and 15 acoustic and kinematic speech measures were derived. Quantitative speech measures were grouped into feature categories ("AOS features," "dysarthria features," "shared features"). In addition to quantitative speech measures, two certified speech-language pathologists made independent, blinded auditory-perceptual ratings of motor speech impairment. A principal component analysis (PCA) was conducted to investigate the relative contributions of quantitative features. RESULTS: Quantitative speech measures were generally concordant with independent clinician ratings of motor speech impairment severity. Hypothesis-driven groupings of quantitative measures differentiated predominantly apraxic from predominantly dysarthric presentations within the MSI+ group. PCA results provided additional evidence for differential profiles of motor speech impairment in the MSI+ group; heterogeneity across individuals is explained in large part by varying levels of overall severity-captured by the shared feature variable group-and degree of apraxia severity, as measured by the AOS feature variable group. CONCLUSIONS: Quantitative features reveal heterogeneity of MSI in the 4RT group in terms of both overall severity and subtype of MSI. Results suggest the potential for acoustic and kinematic speech assessment methods to inform characterization of motor speech impairment in 4RT-associated syndromes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21401778.

Speaking with a KN95 face mask: a within-subjects study on speaker adaptation and strategies to improve intelligibility.

Mask-wearing during the COVID-19 pandemic has prompted a growing interest in the functional impact of masks on speech and communication. Prior work has shown that masks dampen sound, impede visual communication cues, and reduce intelligibility. However, more work is needed to understand how speakers change their speech while wearing a mask and to identify strategies to overcome the impact of wearing a mask. Data were collected from 19 healthy adults during a single in-person session. We investigated the effects of wearing a KN95 mask on speech intelligibility, as judged by two speech-language pathologists, examined speech kinematics and acoustics associated with mask-wearing, and explored KN95 acoustic filtering. We then considered the efficacy of three speaking strategies to improve speech intelligibility: Loud, Clear, and Slow speech. To inform speaker strategy recommendations, we related findings to self-reported speaker effort. Results indicated that healthy speakers could compensate for the presence of a mask and achieve normal speech intelligibility. Additionally, we showed that speaking loudly or clearly-and, to a lesser extent, slowly-improved speech intelligibility. However, using these strategies may require increased physical and cognitive effort and should be used only when necessary. These results can inform recommendations for speakers wearing masks, particularly those with communication disorders (e.g., dysarthria) who may struggle to adapt to a mask but can respond to explicit instructions. Such recommendations may further help non-native speakers and those communicating in a noisy environment or with listeners with hearing loss.

Validity of Off-the-Shelf Automatic Speech Recognition for Assessing Speech Intelligibility and Speech Severity in Speakers With Amyotrophic Lateral Sclerosis.

PURPOSE: There is increasing interest in using automatic speech recognition (ASR) systems to evaluate impairment severity or speech intelligibility in speakers with dysarthria. We assessed the clinical validity of one currently available off-the-shelf (OTS) ASR system (i.e., a Google Cloud ASR API) for indexing sentence-level speech intelligibility and impairment severity in individuals with amyotrophic lateral sclerosis (ALS), and we provided guidance for potential users of such systems in research and clinic. METHOD: Using speech samples collected from 52 individuals with ALS and 20 healthy control speakers, we compared word recognition rate (WRR) from the commercially available Google Cloud ASR API (Machine WRR) to clinician-provided judgments of impairment severity, as well as sentence intelligibility (Human WRR). We assessed the internal reliability of Machine and Human WRR by comparing the standard deviation of WRR across sentences to the minimally detectable change (MDC), a clinical benchmark that indicates whether results are within measurement error. We also evaluated Machine and Human WRR diagnostic accuracy for classifying speakers into clinically established categories. RESULTS: Human WRR achieved better accuracy than Machine WRR when indexing speech severity, and, although related, Human and Machine WRR were not strongly correlated. When the speech signal was mixed with noise (noise-augmented ASR) to reduce a ceiling effect, Machine WRR performance improved. Internal reliability metrics were worse for Machine than Human WRR, particularly for typical and mildly impaired severity groups, although sentence length significantly impacted both Machine and Human WRRs. CONCLUSIONS: Results indicated that the OTS ASR system was inadequate for early detection of speech impairment and grading overall speech severity. While Machine and Human WRR were correlated, ASR should not be used as a one-to-one proxy for transcription speech intelligibility or clinician severity ratings. Overall, findings suggested that the tested OTS ASR system, Google Cloud ASR, has limited utility for grading clinical speech impairment in speakers with ALS.

Characterizing Dysarthria Diversity for Automatic Speech Recognition: A Tutorial from the Clinical Perspective.

Despite significant advancements in automatic speech recognition (ASR) technology, even the best performing ASR systems are inadequate for speakers with impaired speech. This inadequacy may be, in part, due to the challenges associated with acquiring a sufficiently diverse training sample of disordered speech. Speakers with dysarthria, which refers to a group of divergent speech disorders secondary to neurologic injury, exhibit highly variable speech patterns both within and across individuals. This diversity is currently poorly characterized and, consequently, difficult to adequately represent in disordered speech ASR corpora. In this paper, we consider the variable expressions of dysarthria within the context of established clinical taxonomies (e.g., Darley, Aronson, and Brown dysarthria subtypes). We also briefly consider past and recent efforts to capture this diversity quantitatively using speech analytics. Understanding dysarthria diversity from the clinical perspective and how this diversity may impact ASR performance could aid in (1) optimizing data collection strategies for minimizing bias; (2) ensuring representative ASR training sets; and (3) improving generalization of ASR across users and performance for difficult-to-recognize speakers. Our overarching goal is to facilitate the development of robust ASR systems for dysarthric speech using clinical knowledge.

Co-Occurrence of Hypernasality and Voice Impairment in Amyotrophic Lateral Sclerosis: Acoustic Quantification.

PURPOSE: Hypernasality and atypical voice characteristics are common features of dysarthric speech due to amyotrophic lateral sclerosis (ALS). Existing acoustic measures have been developed to primarily target either hypernasality or voice impairment, and the effects of co-occurring hypernasality-voice problems on these measures are unknown. This report explores (a) the extent to which acoustic measures are affected by concurrent perceptually identified hypernasality and voice impairment due to ALS and (b) candidate acoustic measures of early indicators of hypernasality and voice impairment in the presence of multisystem involvement in individuals with ALS. METHOD: Two expert listeners rated severity of hypernasality and voice impairment in sentences produced by individuals with ALS (n = 27). The samples were stratified based on perceptual ratings: voice/hypernasality asymptomatic, predominantly hypernasal, predominantly voice impairment, and mixed (co-occurring hypernasality and voice impairment). Groups were compared using established acoustic measures of hypernasality (one-third octave analysis) and voice (cepstral/spectral analysis) impairment. RESULTS: The one-third octave analysis differentiated all groups; the cepstral peak prominence differentiated all groups except asymptomatic versus mixed, whereas the low-to-high spectral ratio did not differ among groups. Additionally, one-third octave analyses demonstrated promising speech diagnostic potential. CONCLUSIONS: The results highlight the need to consider the validity of measures in the context of multisubsystem involvement. Our preliminary findings further suggest that the one-third octave analysis may be an optimal approach to quantify hypernasality and voice abnormalities in the presence of multisystem speech impairment. Future evaluation of the diagnostic accuracy of the one-third octave analysis is warranted.

Mindfulness Training Improves Cognition and Strengthens Intrinsic Connectivity Between the Hippocampus and Posteromedial Cortex in Healthy Older Adults.

Maintaining optimal cognitive functioning throughout the lifespan is a public health priority. Evaluation of cognitive outcomes following interventions to promote and preserve brain structure and function in older adults, and associated neural mechanisms, are therefore of critical importance. In this randomized controlled trial, we examined the behavioral and neural outcomes following mindfulness training (n = 72), compared to a cognitive fitness program (n = 74) in healthy, cognitively normal, older adults (65-80 years old). To assess cognitive functioning, we used the Preclinical Alzheimer Cognitive Composite (PACC), which combines measures of episodic memory, executive function, and global cognition. We hypothesized that mindfulness training would enhance cognition, increase intrinsic functional connectivity measured with magnetic resonance imaging (MRI) between the hippocampus and posteromedial cortex, as well as promote increased gray matter volume within those regions. Following the 8-week intervention, the mindfulness training group showed improved performance on the PACC, while the control group did not. Furthermore, following mindfulness training, greater improvement on the PACC was associated with a larger increase in intrinsic connectivity within the default mode network, particularly between the right hippocampus and posteromedial cortex and between the left hippocampus and lateral parietal cortex. The cognitive fitness training group did not show such effects. These findings demonstrate that mindfulness training improves cognitive performance in cognitively intact older individuals and strengthens connectivity within the default mode network, which is particularly vulnerable to aging affects. Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/NCT02628548], identifier [NCT02628548].