Artificial Intelligence Tools to Evaluate Language and Speech Patterns in Alzheimer's Disease

Abstract

BackgroundSpeech and language problems are one of the earliest signs of neurodegenerative diseases including Alzheimer’s disease (AD), but they are often difficult to detect especially in the early stages. Utility of assessing speech and language problems using artificial intelligence (AI) has been examined in the past. However, the accuracy of different methods has been variable, preventing incorporation of these techniques in clinical use. In this study, we present different speech‐based machine learning techniques. We focused on the automatic extraction of a wide array of interpretable features from the speech signals, then compared these features across different neurological disorders.MethodSpoken responses to four different tasks (i.e., Cookie Theft Picture (CTP) and Stroop Task) were recorded from 80 participants, 5 with AD, 28 with Parkinson's disease (PD), 18 normal controls (NCs), and the remaining with other neurological diseases (OTRs). We automatically extracted several acoustic and linguistic features using speech and language technologies. We conducted pairwise Kruskal–Wallis tests to assess how speech and language abilities differed between different neurodegenerative disease vs. control groups. We used SciPy Python library.ResultThe total amount of speech time and pauses as well as the average duration and the variance of the number pauses significantly differed between AD and NC and between PD and NC in almost every task (p <0.05). Prosodic features based on F0 contour and F0 on the first and last voiced segment resulted in significant differences across tasks between AD and NC, AD and PD, AD and OTR (p <0.05). The phonological posterior for the phonological classes of consonantal, voiced, labial, nasal, velar, trill, and close significantly differed in almost every task between AD and NC, AD and PD and AD and OTR (p <0.05). Moreover, from the transcriptions of the CTP task, the number of sentences and noun phrases differed significantly between AD and NC, whereas the number of contrastive connectives and the moving average type‐token ratio were significant between NC and OTR (p <0.05).ConclusionAutomated analysis of speech employing machine learning techniques can provide objective assessment of patients with AD that may be useful in distinguishing AD from other neurological disorders.

Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease

“…To provide an exhaustive characterization of the speech of participants with PD, we configured a set of interpretable biomarkers, while some are already proposed in our earlier works (31,41), that encodes cognitive, acoustic, and linguistic information. Table 4 reports the description, the dysfunction connected to the occurrence, and the expected behavior for each biomarker.…”

Interpretable Speech Features vs. DNN Embeddings: What to Use in the Automatic Assessment of Parkinson’s Disease in Multi-lingual Scenarios

“…We adopted two distinct characterizations to represent speech traits connected to the onset and progression of PD. On the one hand, we configured a set of interpretable features, some of them already proposed in our previous works [53,54,55], encoding prosodic, linguistic, and cognitive information. A more detailed list of the interpretable features extracted is provided in the Supplementary Material.…”