Impact of Environmental Noise on A lzheimer’s Disease Detection from Speech: Should You Let a Baby Cry? (2022)

Abstract: Research related to automatically detecting Alzheimer’s disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the acoustics of spontaneous speech, speech processing and machine learning (ML) provide promising techniques for reliably detecting AD. However, speech audio may be affected by different types of background noise and it is important to understand how the noise influences the accuracy of ML models detecting AD from speech. In this paper, we study the effect of fifteen types of environmental noise from five different categories on the performance of four ML models trained with three types of acoustic representations. We perform a thorough analysis showing how ML models and acoustic features are affected by different types of acoustic noise. We show that acoustic noise is not necessarily harmful - certain types of noise are beneficial for AD detection models and help increasing accuracy by up to 4.8%. We provide recommendations on how to utilize acoustic noise in order to achieve the best performance results with the ML models deployed in real world.

## introduction alzheimer's disease (ad) is a progressive neurodegenerative disease that affects over 40 million people worldwide @xcite . current forms of diagnosis are both time consuming and expensive @xcite , which might explain why almost half of those living with ad do not receive a timely diagnosis @xcite . studies have shown that ml methods can be applied to distinguish between speech from healthy and ad participants @xcite @xcite . currently, speech recording for ad-related research typically takes place in a quiet room with a guiding clinician. given that smart-phone technology is rapidly advancing, speech assessments using ml models trained on recordings obtained by smartphones offer a potentially simpleto-administer and inexpensive solution, scalable to the entire population, that can be performed anywhere, including the patient's home @xcite @xcite . however, the problem of model robustness to acoustic noise becomes increasingly important when deploying ml models in real world @xcite . current popular approaches to dealing with acoustic noise in ad detection models involve: 1) eliminating noise using various audio preprocessing techniques @xcite , 2) selecting features that are resilient to asr error/noise @xcite , 3) minimizing the effects of noise with multimodal fusion of features @xcite . all these approaches share a common assumption of acoustic noise being definitely harmful for ml models detecting ad from speech. however, in other ml research areas, such as computer vision or nlp, adding a certain level of natural and artificial noise to data is considered a valid and advantageous practice that helps achieving better performance in tasks like image recognition @xcite ), text generation @xcite and relation classification @xcite , among others. the recent studies in ad classification from transcribed speech show that small levels of linguistic noise do not negatively affect performance of bert-based models @xcite , although there is a difference in predictive power between lexical and syntactic features, when it comes to ad detection from speech @xcite . motivated by the previous work, in this paper we study the effect of acoustic noise on performance of the ml models trained to detect ad from speech. the contributions of this paper are: ## conclusions in this paper, we study the effect of fifteen types of acoustic noise, standard in home environments, on ad classification from speech. we perform a thorough analysis showing how four ml models and three types of acoustic features are affected by different types of acoustic noise. we show that natural environmental noise is not necessarily harmful, with certain types of noise even being beneficial for ad classification performance and helping increase accuracy by up to 4.8%. we provide recommendations on how to utilize acoustic noise in order to achieve the best performance results with the ml models deployed in real world in order to facilitate the use of scalable digital health tools for ad detection from speech. further research is necessary to investigate the effect of more types of acoustic noise common in real world scenarios.