Segmental and Suprasegmental Speech Foundation Models for Classifying Cognitive Risk Factors: Evaluating Out-of-the-Box Performance.

Speech foundation models are remarkably successful in various consumer applications, prompting their extension to clinical use-cases. This is challenged by small clinical datasets, which precludes effective fine-tuning. We tested the efficacy of two models to classify participants by segmental (Wav2Vec2.

Information accumulation on the item versus source test of source monitoring: Insights from diffusion modeling.

Source monitoring involves attributing previous experiences (e.g., studied words as items) to their origins (e.

Responsible development of clinical speech AI: Bridging the gap between clinical research and technology.

This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research.

Automated speech analytics in ALS: higher sensitivity of digital articulatory precision over the ALSFRS-R.

: Although studies have shown that digital measures of speech detected ALS speech impairment and correlated with the ALSFRS-R speech item, no study has yet compared their performance in detecting speech changes. In this study, we compared the performances of the ALSFRS-R speech item and an algorithmic speech measure in detecting clinically important changes in speech. Importantly, the study was part of a FDA submission which received the breakthrough device designation for monitoring ALS; we provide this paper as a roadmap for validating other speech measures for monitoring disease progression.

Electroencephalographic Classification Reveals Atypical Speech Motor Planning in Stuttering Adults.

Purpose: This study explores speech motor planning in adults who stutter (AWS) and adults who do not stutter (ANS) by applying machine learning algorithms to electroencephalographic (EEG) signals. In this study, we developed a technique to holistically examine neural activity differences in speaking and silent reading conditions across the entire cortical surface. This approach allows us to test the hypothesis that AWS will exhibit lower separability of the speech motor planning condition.

Operationalizing Clinical Speech Analytics: Moving From Features to Measures for Real-World Clinical Impact.

Objective: This research note advocates for a methodological shift in clinical speech analytics, emphasizing the transition from high-dimensional representations to clinically validated designed to operationalize clinically relevant constructs of interest. The aim is to enhance model generalizability and clinical applicability in real-world settings.

Method: We outline the challenges of using conventional supervised machine learning models in clinical speech analytics, particularly their limited generalizability and interpretability.

Serum levels of stem cell factor for predicting embryo quality.

We evaluated whether serum stem cell factor (s-SCF) levels just prior to ovulation induction could indicate the ability to develop a top-quality (TQ) blastocyst by day 5. We investigated patients with normal ovarian reserve (NOR), polycystic ovary syndrome (PCOS), diminished ovarian reserve (DOR), or mild endometriosis. Our pilot research suggests a correlation between s-SCF levels and the ability to form TQ blastocysts in patients with mild endometriosis.

Reliability and validity of a widely-available AI tool for assessment of stress based on speech.

Cigna's online stress management toolkit includes an AI-based tool that purports to evaluate a person's psychological stress level based on analysis of their speech, the Cigna StressWaves Test (CSWT). In this study, we evaluate the claim that the CSWT is a "clinical grade" tool via an independent validation. The results suggest that the CSWT is not repeatable and has poor convergent validity; the public availability of the CSWT despite insufficient validation data highlights concerns regarding premature deployment of digital health tools for stress and anxiety management.

Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection.

Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical interest.

Ensembling noisy segmentation masks of blurred sperm images.

Background: Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head.

Wav2DDK: Analytical and Clinical Validation of an Automated Diadochokinetic Rate Estimation Algorithm on Remotely Collected Speech.

Purpose: Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. Remote collection of speech tasks provides a convenient alternative to in-clinic visits, but scoring these assessments can be a laborious process for clinicians. This work describes Wav2DDK, an automated algorithm for estimating the diadochokinetic (DDK) rate on remotely collected audio from healthy participants and participants with amyotrophic lateral sclerosis (ALS).

A speech-based prognostic model for dysarthria progression in ALS.

: We demonstrated that it was possible to predict ALS patients' degree of future speech impairment based on past data. We used longitudinal data from two ALS studies where participants recorded their speech on a daily or weekly basis and provided ALSFRS-R speech subscores on a weekly or quarterly basis (quarter-annually). : Using their speech recordings, we measured articulatory precision (a measure of the crispness of pronunciation) using an algorithm that analyzed the acoustic signal of each phoneme in the words produced.

Speech Entrainment in Adolescent Conversations: A Developmental Perspective.

Purpose: Defined as the similarity of speech behaviors between interlocutors, speech entrainment plays an important role in successful adult conversations. According to theoretical models of entrainment and research on motoric, cognitive, and social developmental milestones, the ability to entrain should develop throughout adolescence. However, little is known about the specific developmental trajectory or the role of speech entrainment in conversational outcomes of this age group.

Language Analytics for Assessment of Mental Health Status and Functional Competency.

Background And Hypothesis: Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling.

TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library.

The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. This means there is a wealth of machine learning tools which are freely available in the Python ecosystem that cannot be easily integrated with DIVA.

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.

Consonant-Vowel Transition Models Based on Deep Learning for Objective Evaluation of Articulation.

Spectro-temporal dynamics of consonant-vowel (CV) transition regions are considered to provide robust cues related to articulation. In this work, we propose an objective measure of precise articulation, dubbed the objective articulation measure (OAM), by analyzing the CV transitions segmented around vowel onsets. The OAM is derived based on the posteriors of a convolutional neural network pre-trained to classify between different consonants using CV regions as input.

X-chromosome inactivation patterns depend on age and tissue but not conception method in humans.

Female somatic X-chromosome inactivation (XCI) balances the X-linked transcriptional dosages between the sexes, randomly silencing the maternal or paternal X chromosome in each cell of 46,XX females. Skewed XCI toward one parental X has been observed in association with ageing and in some female carriers of X-linked diseases. To address the problem of non-random XCI, we quantified the XCI skew in different biological samples of naturally conceived females of different age groups and girls conceived after in vitro fertilization (IVF).

Investigating the Impact of Speech Compression on the Acoustics of Dysarthric Speech.

Acoustic analysis plays an important role in the assessment of dysarthria. Out of a public health necessity, telepractice has become increasingly adopted as the modality in which clinical care is given. While there are differences in software among telepractice platforms, they all use some form of speech compression to preserve bandwidth, with the most common algorithm being the Opus codec.

Mitochondrial DNA Copy Number in Cleavage Stage Human Embryos-Impact on Infertility Outcome.

A retrospective case control study was undertaken at the molecular biology department of a private center for reproductive medicine in order to determine whether any correlation exists between mitochondrial DNA (mtDNA) content of cleavage-stage preimplantation embryos and their developmental potential. A total of 69 couples underwent IVF treatment (averaged women age: 36.5, SD 4.

Applying Civil Rights Law to Clinical Research: Title VI's Equal Access Mandate.

Title VI of the Civil Rights Act of 1964 and its implementing regulations prohibit federally-funded educational institutions and healthcare centers from engaging in disparate impact discrimination "on the ground of race, color, or national origin" in all of their operations.

Automated semantic relevance as an indicator of cognitive decline: Out-of-sample validation on a large-scale longitudinal dataset.

We developed and evaluated an automatically extracted measure of cognition (semantic relevance) using automated and manual transcripts of audio recordings from healthy and cognitively impaired participants describing the Cookie Theft picture from the Boston Diagnostic Aphasia Examination. We describe the rationale and metric validation. We developed the measure on one dataset and evaluated it on a large database (>2000 samples) by comparing accuracy against a manually calculated metric and evaluating its clinical relevance.