ArticleNPJ digital medicine2020
Assessing the accuracy of automatic speech recognition for psychotherapy.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Dictionary-Augmented Large Language Model Postprocessing for Bilingual Code-Switched Medical Speech Recognition: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Speech-Derived Digital Markers of Treatment Response and Improvement Trajectories in Pediatric Anxiety.Research square · 2026Article
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- A gender-emotion interaction multi-task network for depression recognition via transformer-based multimodal fusion.Frontiers in psychiatry · 2026Article
- Benchmarking speech-to-text robustness in noisy emergency medical dialogues: an evaluation of models under realistic acoustic conditions.JAMIA open · 2025Article
- Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts.JMIR formative research · 2025Article
- Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: a Comparative Study of Four AI-Powered Engines.Journal of healthcare informatics research · 2025Article
- Article
- Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency.Biology · 2024Review
- Strategy for developing a speech recognition model specialized for patients with depression or Parkinson's disease with small size speech database.Biomedical engineering letters · 2024Article
- Returning Individual Research Results from Digital Phenotyping in Psychiatry.The American journal of bioethics : AJOB · 2024Article
- The AI Act in a law enforcement context: The case of automatic speech recognition for transcribing investigative interviews.Forensic science international. Synergy · 2024Article
- Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.JMIR mental health · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
- A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency.Npj mental health research · 2022Article
- Expectations for Artificial Intelligence (AI) in Psychiatry.Current psychiatry reports · 2022Review
- Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis.JMIR aging · 2022Article
- Automated evaluation of psychotherapy skills using speech and language technologies.Behavior research methods · 2022Article
- Automatic speech recognition performance for digital scribes: a performance comparison between general-purpose and specialized models tuned for patient-clinician conversations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80% and positive predictive value was 83%. For clinician-identified harm-related sentences, the word error rate was 34%. These results suggest that automatic speech recognition may support understanding of language patterns and subgroup variation in existing treatments but may not be ready for individual-level safety surveillance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.