Search

The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.

This chapter examines state-of-the-art methods for coding, analyzing, and interpreting discourse-level language data from children and adults with language disorders using the data, tools, and methods provided by the TalkBank system (https://www.talkbank.org). These open and free methods have been used for language sample analysis (LSA) with several clinical populations (e.g., child language disorders, stuttering, aphasia, dementia, traumatic brain injury, right hemisphere brain damage), as well as with control participants without communication impairments. We review the six core principles guiding TalkBank, the current shape of the 15 TalkBank databanks, and the different analytic tools provided by TalkBank. We examine automatic TalkBank methods that use ASR (automatic speech recognition), NLP (natural language processing), database technology, statistics in R and Python, and ML (machine learning). The specific tools include corpus analysis methods, LSA profiling systems, online database searches through TalkBank, online browsing through transcripts linked to media, and a new system for online collaborative commentary. These systems provide multimedia access to transcripts from a wide variety of participants with and without language disorders.

Auditory-perceptual rating of connected speech in aphasia (APROCSA) is a system in which trained listeners rate a variety of perceptual features of connected speech samples, representing the disruptions and abnormalities that commonly occur in aphasia. APROCSA has shown promise as an approach for quantifying expressive speech and language function in individuals with aphasia. The aim of this study was to acquire and share a set of audiovisual recordings of connected speech samples from a diverse group of individuals with aphasia, along with consensus ratings of APROCSA features, for future use as training materials to teach others how to use the APROCSA system. Connected speech samples were obtained from six individuals with chronic post-stroke aphasia. The first five minutes of participant speech were excerpted from each sample, and five researchers independently evaluated each sample using APROCSA, rating its 27 features on a five-point scale. The researchers then discussed each feature in turn to obtain consensus ratings. The dataset will provide a useful, freely accessible resource for researchers, clinicians, and students to learn how to evaluate aphasic speech with an auditory-perceptual approach.

Purpose: Individuals with aphasia identify discourse-level communication (i.e., language in use) as a high priority for treatment. The central premise of most aphasia treatments is that restoring language at the phoneme, word, and/or sentence level will generalize to discourse. However, treatment-related changes in discourse-level communication are modest, are poorly understood, and vary greatly among individuals with aphasia. In response, this study consisted of a multilevel discourse analysis of archival, monologic discourse outcomes across two high-intensity Semantic Feature Analysis (SFA) clinical trials. Aim 1 evaluated changes in theoretically motivated discourse outcomes representing lexical-semantic processing, lexical diversity, grammatical complexity, and discourse informativeness. Aim 2 explored the potential moderating role of nonlanguage cognitive factors (semantic memory, divided attention, and executive function) on discourse outcomes. Method: This study was a retrospective analysis of archival monologic discourse outcomes after intensive SFA for n = 60 (Aim 1) and a subset n = 44 (Aim 2). Outcome measures included lexical--semantic processing (% semantic errors), lexical diversity (moving average type--token ratio), grammatical complexity (mean utterance length), and discourse informativeness (% correct information units). Bayesian generalized mixed-effects models were used to examine changes across four study time points: enrollment, entry, exit, and 1-month follow-up. Results: The present study found no evidence for meaningful or statistically reliable improvements in monologue discourse performance after SFA when measured using standard, general-topic discourse stimuli. There was weak and inconsistent evidence that nonlanguage cognitive factors may play a role in moderating treatment response. Conclusions: These findings indicate a clear need to pair theoretically informed treatments designed to facilitate generalization to discourse with intentional measurement paradigms designed to capture it. Furthermore, there is a clear need to examine how established treatments, restorative or compensatory, can better facilitate generalization to discourse-level communication. These priorities are critical for meaningfully improving everyday communication and reducing the profound communication and psychosocial consequences of aphasia.