Purpose: The spatiotemporal index (STI) is a widely used approach for measuring speech pattern stability across multiple repetitions of a stimulus. In this study, we examine how methodological choices in the implementation of the STI (including the number of repetitions, length of stimuli, and parsing procedure) can affect its value. Method: To evaluate how each methodological decision affects the STI, we use a synthetic data framework that allows for the generation of random productions of the template phrase “Buy Bobby a Puppy” at different stability levels. Within this framework, we conduct three experiments: Experiment 1 investigates the effects of the number of repetitions, Experiment 2 investigates the effects of stimulus length, and Experiment 3 investigates the effects of parsing errors. Results: In Experiment 1, we observed that STI values based on fewer repetitions will systematically underestimate larger repetition estimates. Experiment 2 showed that STI values will tend to be higher when calculated on longer (multimovement) stimuli independent of any differences in the stability of the underlying speech patterns. Finally, in Experiment 3, we showed that even minor parsing errors (≈ 10 ms) increase the value of the STI. Conclusions: The results of this study illustrate that even minor choices in the implementation of the STI can have a noticeable impact on the resulting value. These findings highlight the care that needs to be taken when designing studies and comparing STI values across studies to ensure that different STI values are capturing real differences in motion pattern stability rather than trivial methodological variation.

Purpose: This study investigated whether listener processing of dysarthric speech requires the recruitment of more cognitive resources (i.e., higher levels of listening effort) than neurotypical speech. We also explored relationships between behavioral listening effort, perceived listening effort, and objective measures of word transcription accuracy. Method: A word recall paradigm was used to index behavioral listening effort. The primary task involved word transcription, whereas a memory task involved recalling words from previous sentences. Nineteen listeners completed the paradigm twice, once while transcribing dysarthric speech and once while transcribing neurotypical speech. Perceived listening effort was rated using a visual analog scale. Results: Results revealed significant effects of dysarthria on the likelihood of correct word recall, indicating that the transcription of dysarthric speech required higher levels of behavioral listening effort relative to neurotypical speech. There was also a significant relationship between transcription accuracy and measures of behavioral listening effort, such that listeners who were more accurate in understanding dysarthric speech exhibited smaller changes in word recall when listening to dysarthria. The subjective measure of perceived listening effort did not have a statistically significant correlation with measures of behavioral listening effort or transcription accuracy. Conclusions: Results suggest that cognitive resources, particularly listeners' working memory capacity, are more taxed when deciphering dysarthric versus neurotypical speech. An increased demand on these resources may affect a listener's ability to remember aspects of their conversations with people with dysarthria, even when the speaker is fully intelligible.

Silent speech interfaces (SSIs) convert non-audio bio-signals, such as articulatory movement, to speech. This technology has the potential to recover the speech ability of individuals who have lost their voice but can still articulate (e.g., laryngectomees). Articulation-to-speech (ATS) synthesis is an algorithm design of SSI that has the advantages of easy-implementation and low-latency, and therefore is becoming more popular. Current ATS studies focus on speaker-dependent (SD) models to avoid large variations of articulatory patterns and acoustic features across speakers. However, these designs are limited by the small data size from individual speakers. Speaker adaptation designs that include multiple speakers’ data have the potential to address the issue of limited data size from single speakers; however, few prior studies have investigated their performance in ATS. In this paper, we investigated speaker adaptation on both the input articulation and the output acoustic signals (with or without direct inclusion of data from test speakers) using the publicly available electromagnetic articulatory (EMA) dataset. We used Procrustes matching and voice conversion for articulation and voice adaptation, respectively. The performance of the ATS models was measured objectively by the mel-cepstral distortions (MCDs). The synthetic speech samples were generated and are provided in the supplementary material. The results demonstrated the improvement brought by both Procrustes matching and voice conversion on speaker-independent ATS. With the direct inclusion of target speaker data in the training process, the speaker-adaptive ATS achieved a comparable performance to speaker-dependent ATS. To our knowledge, this is the first study that has demonstrated that speaker-adaptive ATS can achieve a non-statistically different performance to speaker-dependent ATS.

A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are useful in a number of applications. Typically, estimating these quantities requires complete knowledge of the underlying distribution followed by multidimensional integration. Existing methods make parametric assumptions about the data distribution or use nonparametric density estimation followed by high-dimensional integration. In this paper, we propose a new alternative. We introduce the concept of “data-driven basis functions”-functions of distributions whose value we can estimate given only samples from the underlying distributions without requiring distribution fitting or direct integration. We derive a new data-driven complete basis that is similar to the deterministic Bernstein polynomial basis and develop two methods for performing basis expansions of functionals of two distributions. We also show that the new basis set allows us to approximate functions of distributions as closely as desired. Finally, we evaluate the methodology by developing data-driven estimators for the Kullback-Leibler divergences and the Hellinger distance and by constructing empirical estimates of tight bounds on the Bayes error rate.

Purpose: The aim of this study was to leverage data-driven approaches, including a novel articulatory consonant distinctiveness space (ACDS) approach, to better understand speech motor control in amyotrophic lateral sclerosis (ALS). Method: Electromagnetic articulography was used to record tongue and lip movement data during the production of 10 consonants from healthy controls ( n = 15) and individuals with ALS ( n = 47). To assess phoneme distinctness, speech data were analyzed using two classification algorithms, Procrustes matching (PM) and support vector machine (SVM), and the area/volume of the ACDS. Pearson's correlation coefficient was used to examine the relationship between bulbar impairment and the ACDS. Analysis of variance was used to examine the effects of bulbar impairment on consonant distinctiveness and consonant classification accuracies in clinical subgroups. Results: There was a significant relationship between the ACDS and intelligible speaking rate (area, p = .003; volume, p = .010), and the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R) bulbar subscore (area, p = .009; volume, p = .027). Consonant classification performance followed a consistent pattern with bulbar severity, where consonants produced by speakers with more severe ALS were classified less accurately (SVM = 75.27%; PM = 74.54%) than the healthy, asymptomatic, and mild–moderate groups. In severe ALS, area of the ACDS was significantly condensed compared to both asymptomatic ( p = .004) and mild–moderate ( p = .013) groups. There was no statistically significant difference in area between the severe ALS group and healthy speakers ( p = .292). Conclusions: Our comprehensive approach is sensitive to early oromotor changes in response due to disease progression. The preserved articulatory consonant space may capture the use of compensatory adaptations to counteract influences of neurodegeneration. Supplemental Material: https://doi.org/10.23641/asha.22044320