This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer’s Dementia (AD). Participants were invited to employ signal processing and machine learning methods to create predictive models based on spontaneous speech data. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek). To the best of our knowledge no work has investigated acoustic features of the speech signal in multilingual AD detection. Our baseline system used conventional machine learning algorithms with Active Data Representation of acoustic features, achieving accuracy of 73.91% on AD detection, and 4.95 root mean squared error on cognitive score prediction.

Aphasia is a language disorder that affects the speaking ability of millions of patients.This paper presents a new benchmark for Aphasia speech recognition and detection tasks using state-of-the-art speech recognition techniques with the Aphsia-Bank dataset.Specifically, we introduce two multi-task learning methods based on the CTC/Attention architecture to perform both tasks simultaneously.Our system achieves state-ofthe-art speaker-level detection accuracy (97.3%), and a relative WER reduction of 11% for moderate Aphasia patients.In addition, we demonstrate the generalizability of our approach by applying it to another disordered speech database, the Demen-tiaBank Pitt corpus.We will make our all-in-one recipes and pre-trained model publicly available to facilitate reproducibility.Our standardized data preprocessing pipeline and open-source recipes enable researchers to compare results directly, promoting progress in disordered speech processing.

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline. This paper presents these prediction tasks in detail, describes the datasets used, and reports the results of the baseline classification and regression models we developed for each task. A combination of acoustic and linguistic features extracted directly from audio recordings, without human intervention, yielded a baseline accuracy of 78.87% for the AD classification task, an MMSE prediction root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive decline prediction task.

The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia based on spontaneous speech can be compared. ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task. In the Alzheimer's speech classification task, ADReSS challenge participants create models for classifying speech as dementia or healthy control speech. In the the neuropsychological score regression task, participants create models to predict mini-mental state examination scores. This paper describes the ADReSS Challenge in detail and presents a baseline for both tasks, including feature extraction procedures and results for classification and regression models. ADReSS aims to provide the speech and language Alzheimer's research community with a platform for comprehensive methodological comparisons. This will hopefully contribute to addressing the lack of standardisation that currently affects the field and shed light on avenues for future research and clinical applicability.

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.The curricula are modeled by a linear ranking function which is the scalar product of a learned weight vector and an engineered feature vector that characterizes the different aspects of the complexity of each instance in the training corpus.We show that learning the curriculum improves performance on a variety of downstream tasks over random orders and in comparison to the natural corpus order.

To facilitate the use of syntactic information in the study of child language acquisition, a coding scheme for Grammatical Relations (GRs) in transcripts of parent-child dialogs has been proposed by Sagae, MacWhinney and Lavie (2004). We discuss the use of current NLP techniques to produce the GRs in this annotation scheme. By using a statistical parser (Charniak, 2000) and memory-based learning tools for classification (Daelemans et al., 2004), we obtain high precision and recall of several GRs. We demonstrate the usefulness of this approach by performing automatic measurements of syntactic development with the Index of Productive Syntax (Scarborough, 1990) at similar levels to what child language researchers compute manually.

Linguistic forms are shaped by forces operating on vastly different time scales. Some of these forces operate directly at the moment of speaking, whereas others accumulate over time in personal and social memory. Our challenge is to understand how forces with very different time scales mesh together in the current moment to determine the emergence of linguistic form.