Optimizing Communication in Ataxia: A Multifaceted Approach to Alternative and Augmentative Communication (AAC).

The progression of multisystem neurodegenerative diseases such as ataxia significantly impacts speech and communication, necessitating adaptive clinical care strategies. With the deterioration of speech, Alternative and Augmentative Communication (AAC) can play an ever increasing role in daily life for individuals with ataxia. This review describes the spectrum of AAC resources available, ranging from unaided gestures and sign language to high-tech solutions like speech-generating devices (SGDs) and eye-tracking technology. Despite the availability of various AAC tools, their efficacy is often compromised by the physical limitations inherent in ataxia, including upper limb ataxia and visual disturbances. Traditional speech-to-text algorithms and eye gaze technology face challenges in accuracy and efficiency due to the atypical speech and movement patterns associated with the disease.In addressing these challenges, maintaining existing speech abilities through rehabilitation is prioritized, complemented by advances in digital therapeutics to provide home-based treatments. Simultaneously, projects incorporating AI driven solutions aim to enhance the intelligibility of dysarthric speech through improved speech-to-text accuracy.This review discusses the complex needs assessment for AAC in ataxia, emphasizing the dynamic nature of the disease and the importance of regular reassessment to tailor communication strategies to the changing abilities of the individual. It also highlights the necessity of multidisciplinary involvement for effective AAC assessment and intervention. The future of AAC looks promising with developments in brain-computer interfaces and the potential of voice banking, although their application in ataxia requires further exploration.

A Virtual Reality Exergame: Clinician-Guided Breathing and Relaxation for Children with Muscular Dystrophy.

This study introduces a VR-based breathing and relaxation exergame tailored for individuals with Duchenne muscular dystrophy (DMD). DMD is a rare neuromuscular disease that leads to respiratory muscle dysfunction with anxiety being a common comorbidity. Clinical management requires frequent visits to rare disease specialists to manage symptom progression. Limited availability and/or proximity of rare disease experts present challenges to care and can lead to missed care opportunities and reduced quality of life. We propose a breathing and relaxation exergame with remote telehealth applicability that incorporates shared patient-clinician VR interaction, and physiological sensors that provide both real-time feedback to the patient and health analytics for the clinician. The game focuses on two key aspects of DMD clinical care that can be mediated through control of breathing: relaxation/mindfulness training and respiratory muscle exercise. The system was evaluated among 13 individuals, including 4 participants with DMD. Feedback surveys, interviews, and focus group discussions with participants, accompanying family members, and clinicians demonstrated the feasibility of this VR tool for telehealth or as part of a home exercise program.

Empirical Evaluation of the Role of Vocal Fold Collision on Relative Fundamental Frequency in Voicing Offset.

OBJECTIVES: Relative fundamental frequency (RFF) is an acoustic measure of changes in fundamental frequency during voicing transitions. The physiological mechanisms underlying RFF remain unclear. Recent modeling suggests that changes in RFF during voicing offset are due to decreases in overall system stiffness as a direct result of the cessation of vocal fold collision. To evaluate this finding empirically, here we examined whether variable timing between the end of vocal fold collision and the final voicing cycle used to calculate RFF explained the variability in RFF across individual voicing offset utterances. METHODS: RFF during voicing offset was calculated from /ifi/ utterances produced by 35 participants under endoscopy, with and without vocal effort. RFF was calculated via two methods, in which utterances were aligned by (1) the end of vocal fold collision, or (2) the end of voicing. Analyses of variance were used to determine the effects of vocal effort and RFF method on the mean and standard deviation of RFF. RESULTS: Aligning by vocal fold collision resulted in statistically significantly lower standard deviations. RFF means were statistically higher using the collision method; however, the degree of vocal effort was statistically significant regardless of the method. CONCLUSIONS: These results provide empirical evidence to support that decreases in RFF during voicing offset are a result of decreases in system stiffness due to termination of vocal fold collision.

Ability-Based Methods for Personalized Keyboard Generation.

This study introduces an ability-based method for personalized keyboard generation, wherein an individual's own movement and human-computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user's movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual's movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user's motor abilities when designing virtual interfaces.

Ability-based Keyboards for Augmentative and Alternative Communication: Understanding How Individuals' Movement Patterns Translate to More Efficient Keyboards: Methods to Generate Keyboards Tailored to User-specific Motor Abilities.

This study presents the evaluation of ability-based methods extended to keyboard generation for alternative communication in people with dexterity impairments due to motor disabilities. Our approach characterizes user-specific cursor control abilities from a multidirectional point-select task to configure letters on a virtual keyboard based on estimated time, distance, and direction of movement. These methods were evaluated in three individuals with motor disabilities against a generically optimized keyboard and the ubiquitous QWERTY keyboard. We highlight key observations relating to the heterogeneity of the manifestation of motor disabilities, perceived importance of communication technology, and quantitative improvements in communication performance when characterizing an individual's movement abilities to design personalized AAC interfaces.

Prediction of Voice Fundamental Frequency and Intensity from Surface Electromyographic Signals of the Face and Neck.

Silent speech interfaces (SSIs) enable speech recognition and synthesis in the absence of an acoustic signal. Yet, the archetypal SSI fails to convey the expressive attributes of prosody such as pitch and loudness, leading to lexical ambiguities. The aim of this study was to determine the efficacy of using surface electromyography (sEMG) as an approach for predicting continuous acoustic estimates of prosody. Ten participants performed a series of vocal tasks including sustained vowels, phrases, and monologues while acoustic data was recorded simultaneously with sEMG activity from muscles of the face and neck. A battery of time-, frequency-, and cepstral-domain features extracted from the sEMG signals were used to train deep regression neural networks to predict fundamental frequency and intensity contours from the acoustic signals. We achieved an average accuracy of 0.01 ST and precision of 0.56 ST for the estimation of fundamental frequency, and an average accuracy of 0.21 dB SPL and precision of 3.25 dB SPL for the estimation of intensity. This work highlights the importance of using sEMG as an alternative means of detecting prosody and shows promise for improving SSIs in future development.

Effects of Age and Parkinson's Disease on the Relationship between Vocal Fold Abductory Kinematics and Relative Fundamental Frequency.

PURPOSE: This study reports on two experiments to examine vocal fold abduction and its relationship with relative fundamental frequency (RFF), considering two attributes that have been shown to elicit group differences in RFF: age (Experiment 1) and Parkinson's disease (PD; Experiment 2). METHODS: For both experiments, simultaneous acoustic and nasendoscopic recordings were collected as participants produced the utterance, /ifi/. RFF values were computed from the acoustic signal, whereas abduction duration and glottic angle at voicing offset were identified from the laryngoscopic images. In Experiment 1, 50 speakers with typical voices (18-83 years) were analyzed to examine (1A) the effects of speaker age on individual outcome measures (RFF, abduction duration, glottic angle) via Pearson's correlation coefficients, and (1B) the effects of abductory measures and age on RFF via an analysis of covariance. In Experiment 2, 20 speakers with PD and 20 matched controls were analyzed to examine (2A) the effects of group (with/without PD) on outcome measures via an analysis of variance, and (2B) the relationship of RFF with abduction duration, glottic angle, and age when considering group via an analysis of covariance. RESULTS: Age demonstrated a significant, negative relationship with glottic angle (1A) but was not a significant factor when examining the relationship of vocal fold abduction and RFF (1B). Speaker group (with/without PD) demonstrated a significant effect on measures of RFF and abduction duration (2A) but was not a significant factor when examining the relationship of vocal fold abduction and RFF (2B). CONCLUSIONS: RFF is sensitive to changes in vocal fold abductory patterns during devoicing, irrespective of speaker age or PD status.

Clinical Cutoff Scores for Acoustic Indices of Vocal Hyperfunction That Combine Relative Fundamental Frequency and Cepstral Peak Prominence.

PURPOSE: This study examined the discriminative ability of acoustic indices of vocal hyperfunction combining smoothed cepstral peak prominence (CPPS) and relative fundamental frequency (RFF). METHOD: Demographic, CPPS, and RFF parameters were entered into logistic regression models trained on two 1:1 case-control groups: individuals with and without nonphonotraumatic vocal hyperfunction (NPVH; n = 360) and phonotraumatic vocal hyperfunction (PVH; n = 240). Equations from the final models were used to predict group membership in two independent test sets (n = 100 each). RESULTS: Both CPPS and RFF parameters significantly improved model fits for NPVH and PVH after accounting for demographics. CPPS explained unique variance beyond RFF in both models. RFF explained unique variance beyond CPPS in the PVH model. Final models included CPPS and RFF offset parameters for both NPVH and PVH; RFF onset parameters were significant only in the PVH model. Area under the receiver operating characteristic curve analysis for the independent test sets revealed acceptable classification for NPVH (72%) and good classification for PVH (86%). CONCLUSIONS: A combination of CPPS and RFF parameters showed better discriminative ability than either measure alone for PVH. Clinical cutoff scores for acoustic indices of vocal hyperfunction are proposed for assessment and screening purposes.

Automated Relative Fundamental Frequency Algorithms for Use With Neck-Surface Accelerometer Signals.

OBJECTIVE: Relative fundamental frequency (RFF) has been suggested as a potential acoustic measure of vocal effort. However, current clinical standards for RFF measures require time-consuming manual markings. Previous semi-automated algorithms have been developed to calculate RFF from microphone signals. The current study aimed to develop fully automated algorithms to calculate RFF from neck-surface accelerometer signals for ecological momentary assessment and ambulatory monitoring of voice. METHODS: Training a set of 2646 /vowel-fricative-vowel/ utterances from 317 unique speakers, with and without voice disorders, was used to develop automated algorithms to calculate RFF values from neck-surface accelerometer signals. The algorithms first rejected utterances with poor vowel-to-noise ratios, then identified fricative locations, then used signal features to determine voicing boundary cycles, and finally calculated corresponding RFF values. These automated RFF values were compared to the clinical gold-standard of manual RFF calculated from simultaneously collected microphone signals in a novel test set of 639 utterances from 77 unique speakers. RESULTS: Automated accelerometer-based RFF values resulted in an average mean bias error (MBE) across all cycles of 0.027 ST, with an MBE of 0.152 ST and -0.252 ST in the offset and onset cycles closest to the fricative, respectively. CONCLUSION: All MBE values were smaller than the expected changes in RFF values following successful voice therapy, suggesting that the current algorithms could be used for ecological momentary assessment and ambulatory monitoring via neck-surface accelerometer signals.

Relative Fundamental Frequency in Individuals with Globus Syndrome and Muscle Tension Dysphagia.

OBJECTIVE: Relative fundamental frequency (RFF) has been investigated as an acoustic measure to assess for changes in laryngeal tension. This study aimed to assess RFF in individuals with globus syndrome, individuals with muscle tension dysphagia (MTDg), and individuals with typical voices. METHODS: RFF values were calculated from the speech acoustics of individuals with globus syndrome (n = 12), individuals with MTDg (n = 12), and age- and sex-matched controls with typical voices (n = 24). An analysis of variance was performed on RFF values to assess the effect of group. RESULTS: There was no statistically significant effect of group on RFF values, with similar values for individuals with globus syndrome, individuals with MTDg, and control participants. CONCLUSIONS: These results suggest that individuals with these disorders do not appear to possess paralaryngeal muscle tension in a locus and/or manner that directly impacts voice production.

Vocal fold kinematics and relative fundamental frequency as a function of obstruent type and speaker age.

The acoustic measure, relative fundamental frequency (RFF), has been proposed as an objective metric for assessing vocal hyperfunction; however, its underlying physiological mechanisms have not yet been fully characterized. This study aimed to characterize the relationship between RFF and vocal fold kinematics. Simultaneous acoustic and high-speed videoendoscopic (HSV) recordings were collected as younger and older speakers repeated the utterances /ifi/ and /iti/. RFF values at voicing offsets and onsets surrounding the obstruents were estimated from acoustic recordings, whereas glottal angles, durations of voicing offset and onset, and a kinematic estimate of laryngeal stiffness (KS) were obtained from HSV images. No differences were found between younger and older speakers for any measure. RFF did not differ between the two obstruents at voicing offset; however, fricatives necessitated larger glottal angles and longer durations to devoice. RFF values were lower and glottal angles were greater for stops relative to fricatives at voicing onset. KS values were greater in stops relative to fricatives. The less adducted vocal folds with greater KS and lower RFF at voicing onset for stops relative to fricatives in this study were in accordance with prior speculations that decreased vocal fold contact area and increased laryngeal stiffness may decrease RFF.

Surface Electromyography-Based Recognition, Synthesis, and Perception of Prosodic Subvocal Speech.

Purpose This study aimed to evaluate a novel communication system designed to translate surface electromyographic (sEMG) signals from articulatory muscles into speech using a personalized, digital voice. The system was evaluated for word recognition, prosodic classification, and listener perception of synthesized speech. Method sEMG signals were recorded from the face and neck as speakers with (n = 4) and without (n = 4) laryngectomy subvocally recited (silently mouthed) a speech corpus comprising 750 phrases (150 phrases with variable phrase-level stress). Corpus tokens were then translated into speech via personalized voice synthesis (n = 8 synthetic voices) and compared against phrases produced by each speaker when using their typical mode of communication (n = 4 natural voices, n = 4 electrolaryngeal [EL] voices). Naïve listeners (n = 12) evaluated synthetic, natural, and EL speech for acceptability and intelligibility in a visual sort-and-rate task, as well as phrasal stress discriminability via a classification mechanism. Results Recorded sEMG signals were processed to translate sEMG muscle activity into lexical content and categorize variations in phrase-level stress, achieving a mean accuracy of 96.3% (SD = 3.10%) and 91.2% (SD = 4.46%), respectively. Synthetic speech was significantly higher in acceptability and intelligibility than EL speech, also leading to greater phrasal stress classification accuracy, whereas natural speech was rated as the most acceptable and intelligible, with the greatest phrasal stress classification accuracy. Conclusion This proof-of-concept study establishes the feasibility of using subvocal sEMG-based alternative communication not only for lexical recognition but also for prosodic communication in healthy individuals, as well as those living with vocal impairments and residual articulatory function. Supplemental Material https://doi.org/10.23641/asha.14558481.

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets based on Vocal Fold Vibratory Measures.

Methods for automating relative fundamental frequency (RFF)-an acoustic estimate of laryngeal tension-rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced-unvoiced-voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm ("aRFF-APH"). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm ("aRFF-AP"), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.

Cursor Click Modality in an Accelerometer-Based Computer Access Device.

The purpose of this study is to investigate the effects of different cursor click modalities in an alternative computer access device using accelerometry from head tilt to control cursor movement. Eighteen healthy adults performed a target acquisition task using the device with five different cursor click modalities, while maintaining cursor movement control via accelerometry. Three dwell-based click modalities with dwell times of 0.5 s, 1.0 s, and 1.5 s were tested. Two surface electromyography-based click modalities - with the sensor placed next to the eye for a blink and above the eyebrow for a brow raise - were tested. Performance was evaluated using metrics of target selection accuracy, path efficiency, target selection time, and user effort. Surface electromyography-based click modalities were as fast as the shortest dwell time and as accurate as the longest dwell time, and also minimized user effort. Three of the four performance metrics were not affected by sensor location. Future studies will investigate if these results are similar in individuals with neuromuscular disorders.

Integrated Head-Tilt and Electromyographic Cursor Control.

This study evaluated the performance of two alternate computer access methods that could be used for two-dimensional cursor control. The first method, ACC/sEMG, integrates head acceleration and facial surface electromyography. The second method, Camera Mouse, is a free-to-use, computer vision-based access method. Twenty-four healthy adults performed a target acquisition task using each computer access method across two lighting conditions and three computer orientations. Performance in the task was evaluated using metrics of target selection accuracy, movement time, and path efficiency. Using ACC/sEMG resulted in better mean path efficiency and target selection accuracy, whereas using Camera Mouse resulted in faster target selection. Moreover, performance in the task when using Camera Mouse depended on lighting conditions in the room. The findings of this study show that the ACC/sEMG system is an effective computer access method across different lighting conditions and computer orientations. However, there is a tradeoff between speed and accuracy: ACC/sEMG system provided higher target selection accuracy compared to Camera Mouse, while the latter provided faster target selection. Future development should focus on evaluating performance of each method in populations with limited motor abilities.

Refining algorithmic estimation of relative fundamental frequency: Accounting for sample characteristics and fundamental frequency estimation method.

Relative fundamental frequency (RFF) is a promising acoustic measure for evaluating voice disorders. Yet, the accuracy of the current RFF algorithm varies across a broad range of vocal signals. The authors investigated how fundamental frequency (fo) estimation and sample characteristics impact the relationship between manual and semi-automated RFF estimates. Acoustic recordings were collected from 227 individuals with and 256 individuals without voice disorders. Common fo estimation techniques were compared to the autocorrelation method currently implemented in the RFF algorithm. Pitch strength-based categories were constructed using a training set (1158 samples), and algorithm thresholds were tuned to each category. RFF was then computed on an independent test set (291 samples) using category-specific thresholds and compared against manual RFF via mean bias error (MBE) and root-mean-square error (RMSE). Auditory-SWIPE' for fo estimation led to the greatest correspondence with manual RFF and was implemented in concert with category-specific thresholds. Refining fo estimation and accounting for sample characteristics led to increased correspondence with manual RFF [MBE = 0.01 semitones (ST), RMSE = 0.28 ST] compared to the unmodified algorithm (MBE = 0.90 ST, RMSE = 0.34 ST), reducing the MBE and RMSE of semi-automated RFF estimates by 88.4% and 17.3%, respectively.

The Effects of Modulating Fundamental Frequency and Speech Rate on the Intelligibility, Communication Efficiency, and Perceived Naturalness of Synthetic Speech.

Purpose This study investigated how modulating fundamental frequency (f0) and speech rate differentially impact the naturalness, intelligibility, and communication efficiency of synthetic speech. Method Sixteen sentences of varying prosodic content were developed via a speech synthesizer. The f0 contour and speech rate of these sentences were altered to produce 4 stimulus sets: (a) normal rate with a fixed f0 level, (b) slow rate with a fixed f0 level, (c) normal rate with prosodically natural f0 variation, and (d) normal rate with prosodically unnatural f0 variation. Sixteen listeners provided orthographic transcriptions and judgments of naturalness for these stimuli. Results Sentences with f0 variation were rated as more natural than those with a fixed f0 level. Conversely, sentences with a fixed f0 level demonstrated higher intelligibility than those with f0 variation. Speech rate did not affect the intelligibility of stimuli with a fixed f0 level. Communication efficiency was highest for sentences produced at a normal rate and a fixed f0 level. Conclusions Sentence-level f0 variation increased naturalness ratings of synthesized speech, whether the variation was prosodically natural or not. However, these f0 variations reduced intelligibility. There is evidence of a trade-off in naturalness and intelligibility of synthesized speech, which may impact future speech synthesis designs. Supplemental Material https://doi.org/10.23641/asha.8847833.

Optimized and Predictive Phonemic Interfaces for Augmentative and Alternative Communication.

Purpose We empirically assessed the results of computational optimization and prediction in communication interfaces that were designed to allow individuals with severe motor speech disorders to select phonemes and generate speech output. Method Interface layouts were either random or optimized, in which phoneme targets that were likely to be selected together were located in proximity. Target sizes were either static or predictive, such that likely targets were dynamically enlarged following each selection. Communication interfaces were evaluated by 36 users without motor impairments using an alternate access method. Each user was assigned to 1 of 4 interfaces varying in layout and whether prediction was implemented (random/static, random/predictive, optimized/static, optimized/predictive) and participated in 12 sessions over a 3-week period. Six participants with severe motor impairments used both the optimized/static and optimized/predictive interfaces in 1-2 sessions. Results In individuals without motor impairments, prediction provided significantly faster communication rates during training (Sessions 1-9), as users were learning the interface target locations and the novel access method. After training, optimization acted to significantly increase communication rates. The optimization likely became relevant only after training when participants knew the target locations and moved directly to the targets. Participants with motor impairments could use the interfaces with alternate access methods and generally rated the interface with prediction as preferred. Conclusions Optimization and prediction led to increases in communication rates in users without motor impairments. Predictive interfaces were preferred by users with motor impairments. Future research is needed to translate these results into clinical practice. Supplemental Material https://doi.org/10.23641/asha.8636948.

Adductory Vocal Fold Kinematic Trajectories During Conventional Versus High-Speed Videoendoscopy.

Objective Prephonatory vocal fold angle trajectories may supply useful information about the laryngeal system but were examined in previous studies using sigmoidal curves fit to data collected at 30 frames per second (fps). Here, high-speed videoendoscopy (HSV) was used to investigate the impacts of video frame rate and sigmoidal fitting strategy on vocal fold adductory patterns for voicing onsets. Method Twenty-five participants with healthy voices performed /ifi/ sequences under flexible nasendoscopy at 1,000 fps. Glottic angles were extracted during adduction for voicing onset; resulting vocal fold trajectories (i.e., changes in glottic angle over time) were down-sampled to simulate different frame rate conditions (30-1,000 fps). Vocal fold adduction data were fit with asymmetric sigmoids using 5 fitting strategies with varying parameter restrictions. Adduction trajectories and maximum adduction velocities were compared between the fits and the actual HSV data. Adduction trajectory errors between HSV data and fits were evaluated using root-mean-square error and maximum angular velocity error. Results Simulated data were generally well fit by sigmoid models; however, when compared to the actual 1,000-fps data, sigmoid fits were found to overestimate maximum angle velocities. Errors decreased as frame rate increased, reaching a plateau by 120 fps. Conclusion In healthy adults, vocal fold kinematic behavior during adduction is generally sigmoidal, although such fits can produce substantial errors when data are acquired at frame rates lower than 120 fps.

Prediction of Optimal Facial Electromyographic Sensor Configurations for Human-Machine Interface Control.

Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.