Summary: Psychologists are turning to artificial intelligence to uncover hidden psychological cues in speech, from word choice to tone and pacing. These signals can reveal personality traits and even early signs of mental health conditions, but human clinicians may miss them.<\/p>\n
AI could provide faster, more accurate analysis, though researchers warn of bias if models aren\u2019t trained on diverse data. With careful development, AI could transform psychological assessment, offering powerful new tools to support clinicians.<\/p>\n
Key Facts<\/p>\n
Language as Data: Speech patterns reveal personality and mental health clues.AI Advantage: Models analyze subtle cues faster and more comprehensively than humans.Bias Challenge: Fair training across diverse populations is essential for reliable use.<\/p>\n
Source: WUSTL<\/p>\n
Words are windows into the brain. The words that we choose \u2014 and how we say them \u2014 speak volumes about our personalities and even our mental health, said WashU psychologist\u00a0Josh Oltmanns. \u201cOur thoughts, feelings and behaviors are reflected in language,\u201d he said.<\/p>\n
Instead of subjecting people to endless batteries of tests, psychologists could gain valuable insights from samples of language. But they might need high-tech help to find the right signals in all of the chatter.<\/p>\n
Artificial intelligence (AI) tools trained to detect tell-tale signs in speech could revolutionize psychological assessment, he said.<\/p>\n
\u201cPsychologists are people, and people are fallible, so even a good clinician might not always pick up on important verbal cues,\u201d said Oltmanns, an assistant professor of psychological and brain sciences in Arts & Sciences at Washington University in St. Louis. \u201cBut a properly trained computer model will catch those cues.\u201d<\/p>\n
In theory, a psychologist could ask a client to describe their life and concerns, a standard part of an initial assessment. In addition to using their own clinical expertise, the psychologist could feed that conversation into a program designed to detect personality traits and signs of mental health concerns.<\/p>\n
\u201cThe computer program could help validate their observations or warn them about something they might have missed,\u201d Oltmanns said.<\/p>\n
Oltmanns is working with his collaborators, who are also WashU PhD students,\u00a0Tu Do<\/a>,\u00a0Tong Li, and\u00a0Tongyao Ran, to develop AI tools to help psychologists uncover these hidden cues in language.<\/p>\n He recently described the potential of such tools in the journal\u00a0Advances in Methods and Practices in Psychological Science. Mehak Gupta, of Southern Methodist University, and Jocelyn Brickman, of Xavier University, were co-authors.<\/p>\n Language can convey psychology in many ways. Word choice matters, whether in a deep conversation or a casual post on X or Facebook. Early in his career, Oltmanns studied how word choices in social media posts broadly reflected a person\u2019s Big Five personality traits: openness to experience, neuroticism, agreeableness, conscientiousness and extraversion.<\/p>\n But the way a person says words matters, too. \u201cYou can tell a lot by how a person speaks,\u201d Oltmanns said. \u201cSlowed speech can be a symptom of depression, while overly rapid speech is associated with anxiety.\u201d<\/p>\n Speed is just one measure. Spoken words also vary in loudness, tone and pitch.\u00a0\u201cSpeech samples have hundreds of different acoustic parameters that could be meaningful,\u201d he said.<\/p>\n With so much potential information buried in each conversation, psychologists have long wanted the help of computers to analyze speech. More than 20 years ago, researchers developed Linguistic Inquiry and Word Count, a software program that could score people on various psychological aspects based on written text. Those tools have been refined over time, but the advent of AI opens a new world of possibilities, Oltmanns said.<\/p>\n \u201cAI programs could be far faster, more thorough and more accurate than previous computer models,\u201d he said.<\/p>\n Still, Oltmanns cautioned that AI also has risks. \u201cIt\u2019s often trained on information on the internet, which means it can be biased,\u201d he said. If those biases aren\u2019t addressed, it\u2019s possible that certain cultural differences in speech patterns could be inaccurately labeled as signs of mental health problems.<\/p>\n To avoid such problems with bias, AI models should be trained on diverse patient populations. To that end, Oltmanns is studying the hundreds of hours of interviews collected over the years through the\u00a0SPAN Study, an ongoing investigation of more than 1,600 St. Louis adults who represent the city\u2019s diversity.<\/p>\n \u201cWe\u2019re particularly interested in looking at speech patterns in white and Black participants to ensure that the AI models treat each group fairly,\u201d he said.<\/p>\n Oltmanns sees several other important questions moving forward. It\u2019s not clear how word choice in written language differs from word choice in speech, or how many words it takes to truly gain insights into a person\u2019s psychology. \u201cWe have a lot of ideas and a lot of work to do,\u201d he said.<\/p>\n Given the speed of innovation in the AI field, he hopes to find answers sooner rather than later.<\/p>\n \u201cCompanies are already selling AI psychological assessment tools to hospitals and clinicians, but it\u2019s not clear to me how well they work or how thoroughly they\u2019ve been evaluated,\u201d Oltmanns said. \u201cThis sort of technology could be a huge advance for the field of psychology, but it has to be done carefully. We have to be smart.\u201d<\/p>\n About this AI and speech research news<\/p>\n Author: Leah Shaffer<\/a>
Source: WUSTL<\/a>
Contact: Leah Shaffer \u2013 WUSTL
Image: The image is credited to Neuroscience News<\/p>\n