Paralinguistic and linguistic fluency features for Alzheimer's disease detection

Abstract

Alzheimer’s disease (AD) is one of the most common forms of dementia in the world. The Mini-Mental State Examination, a tool developed to detect AD, is composed of various tests that evaluate functional performance in several fields, one of which is language. Several symptoms are manifested in voices as a result of language and speech problems caused by AD, including frequent involuntary pauses during conversations and diction and vocabulary difficulties. Speech fluency is considered a key feature for AD detection in this research, for which two algorithms are proposed. The first algorithm is a paralinguistic system that is independent of the language and task and whose low-dimension feature vectors facilitate the training stage. This algorithm is tested on two databases (AcceXible and ADReSS), on two languages (Spanish and English) and on several tests. The second algorithm is based on analysing temporal patterns of silence between words and errors in spoken words. This approach, based on verbal fluency tests, is tested on the AcceXible database. To benchmark these algorithms, two baseline algorithms are used: the i-vector framework, a speaker modelling algorithm that has been effectively used for speech-related tasks such as speaker recognition, language identification, speaker diarization and speech-related health tasks; and a classic counting-terms algorithm, which processes transcriptions of speech. The paralinguistic system yields promising results for different tests and languages, while the silence-based system achieves high accuracy in verbal fluency tests.