All articles tagged with #speech analysis
Research shows that analyzing natural speech patterns, such as pauses and fillers, can serve as a sensitive, non-invasive indicator of brain health and executive function, potentially aiding early detection of cognitive decline and dementia.
Research shows that subtle features in everyday speech, analyzed using AI, can predict executive function and early cognitive decline, offering a scalable, non-invasive tool for monitoring brain health and potentially detecting dementia risk earlier than traditional methods.
Psychologists are exploring how AI can analyze speech patterns to uncover personality traits and early signs of mental health issues, offering faster and potentially more accurate assessments. However, concerns about bias and the need for diverse training data remain, emphasizing careful development and validation of these tools to transform psychological evaluation.
A new AI framework called CTCAIT can detect neurological disorders like Parkinson's, Huntington's, and Wilson disease with over 90% accuracy by analyzing speech patterns, offering a promising non-invasive tool for early diagnosis and monitoring across multiple languages.
Researchers at Boston University have developed an AI algorithm that analyzes speech patterns to predict the progression from mild cognitive impairment (MCI) to Alzheimer's disease with 78.5% accuracy. This method, which uses transcribed audio recordings, could allow for early detection and intervention, potentially through a smartphone app. Early diagnosis is crucial for managing Alzheimer's and participating in clinical trials, offering a better understanding of the disease's progression.
Researchers at Boston University have developed an AI model that predicts the progression from mild cognitive impairment to Alzheimer's disease within six years with 78.5% accuracy by analyzing subtle speech patterns. This non-invasive method, based on data from the Framingham Heart Study, offers a more accessible alternative to traditional diagnostic methods like brain imaging. The study highlights the potential of AI in early diagnosis and treatment, though further validation across diverse populations is needed.
Researchers have conducted a study using artificial intelligence (AI) to analyze speech patterns and predict intoxication levels with 98% accuracy. Participants were given alcohol and asked to recite tongue twisters while their breath alcohol levels were recorded. The AI program was able to determine if someone was within legal sobriety limits, suggesting potential applications such as ignition locks on cars and managing alcohol consumption in bars and restaurants. However, the study had a small sample size and lacked diversity, highlighting the need for further testing.
Researchers used natural language processing (NLP) and artificial intelligence (AI) to analyze the speech of Parkinson's disease patients and found distinct patterns, including the use of more verbs but fewer nouns and fillers. These patterns were detectable even before cognitive decline typically associated with PD was evident, offering a potential new tool for early diagnosis. The analysis was capable of identifying Parkinson's patients with over 80% precision, underscoring the potential of NLP and AI in enhancing healthcare diagnostics.
Startup Accexible has developed a product that assesses the linguistic content of someone’s speech and how they’re speaking to identify changes that may indicate an underlying problem, such as early signs of Alzheimer's. The app promises 90% accuracy and results in just a few minutes, making it a useful screening test for general practitioners and a monitoring tool for neurologists. The company is already being used by 10,000 patients and 40 practitioners across five markets, and is running pilots in Spain, Colombia, and the UK.