Determining the Analysis of Speech in Parkinson’s Disease

The characteristics of speech among patients with Parkinson’s disease (PD) were assessed by a research group. This was done using artificial intelligence (AI) to process natural language.

Image Credit:

AI analysis of their data identified that such patients spoke using fewer nouns and more verbs and fillers.

The study was headed by Professor Masahisa Katsuno and Dr. Katsunori Yokoi, Nagoya University Graduate School of Medicine, in partnership with Aichi Prefectural University and the Toyohashi University of Technology.

The researchers published their study outcomes in the Parkinsonism & Related Disorders journal.

Natural language processing (NLP) technology is known as a branch of AI that concentrates on allowing computers to comprehend and decipher huge amounts of human language data with the help of statistical models for patterns to be determined.

Provided that patients with PD suffer from a variety of speech-related problems, such as language use and impaired speech production, the group utilizes NLP to examine variations in patient speech patterns depending on 37 characteristics by making use of texts made from free conversations.

The analysis disclosed that patients with PD utilized fewer proper nouns, common nouns, and fillers for every sentence. At the same time, they spoke using a greater percentage of verbs and differences for case particles (a significant feature of the Japanese language) per sentence.

When I asked them to talk about their day in the morning, a PD patient might say something like the following, for example: ‘I woke up at 4:50 am. I thought it was a bit early, but I got up. It took me about half an hour to go to the toilet, so I washed up and got dressed around 5.30 am. My husband cooked breakfast. I had breakfast after 6 am. Then I brushed my teeth and got ready to go out.’

Dr. Katsunori Yokoi, Graduate School of Medicine, Nagoya University

Yokoi continued, “Whereas someone from the healthy control group might say something like this: ‘Well, in the morning, I woke up at six o'clock, and got dressed, and, yeah, washed my face. Then, I fed my cat and dog. My daughter prepared a meal, but I told her I couldn't eat, and I, umm, drank some water.’”

While these are examples that we created of conversations reflecting the characteristics of people with PD and healthy people, what you should see is that the total length is similar. However, PD patients speak shorter sentences than people in the control group, leading to more verbs in the machine learning analysis. The healthy control also uses more fillers, such as ‘well’ or, ‘umm’, to connect sentences,” added Yokoi.

The most prospective factor of this study is that the group experimented on patients who did not yet reveal the characteristic cognitive decline observed in PD. Hence, their results provide a possible way of early detection to categorize PD patients. 

Our results suggest that even in the absence of cognitive decline, the conversations of patients with PD differed from those of healthy subjects. When we attempted to identify PD patients or healthy controls based on these conversational changes, we could identify PD patients with over 80% precision. This result suggests the possibility of language analysis using natural language processing to diagnose PD.

Masahisa Katsuno, Professor, Graduate School of Medicine, Nagoya University

Journal Reference

Yokoi, K., et al. (2023) Analysis of spontaneous speech in Parkinson’s disease by natural language processing. Parkinsonism & Related Disorders.


Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Azthena logo powered by Azthena AI

Your AI Assistant finding answers from trusted AZoM content

Azthena logo with the word Azthena

Your AI Powered Scientific Assistant

Hi, I'm Azthena, you can trust me to find commercial scientific answers from

A few things you need to know before we start. Please read and accept to continue.

  • Use of “Azthena” is subject to the terms and conditions of use as set out by OpenAI.
  • Content provided on any AZoNetwork sites are subject to the site Terms & Conditions and Privacy Policy.
  • Large Language Models can make mistakes. Consider checking important information.

Great. Ask your question.

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.