Archive for Cinical applications

Using automatic speech-to-text in clinical applications

A colleague pointed me to Terje Holmlund et al., "Applying speech technologies to assess verbal memory in patients with serious mental illness", NPJ digital medicine 2020:

Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73–0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.

This is great work, but over-interpretation of such results is likely to be a problem. At this stage in the development of the technologies, experimenting with with speech-to-text in such applications is a very good idea, but relying on it without accurate human-corrected transcripts is a very bad idea.

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