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.

心理治疗中录音的准确转录将提高治疗效果、临床医生培训和安全监测。尽管自动语音识别软件已经上市，但其在心理健康环境中的准确性尚未得到很好的描述。目前尚不清楚哪些指标和阈值适合不同的临床用例，这些用例的范围可能从人群描述到个体安全监测。在这里，我们证明自动语音识别在心理治疗中是可行的，但在广泛使用之前需要进一步提高准确性。我们符合 HIPAA 标准的自动语音识别系统的转录单词错误率为 25%。对于抑郁相关话语，敏感性为 80%，阳性预测值为 83%。对于临床医生识别的与伤害相关的句子，单词错误率为 34%。这些结果表明，自动语音识别可能有助于理解现有治疗中的语言模式和亚组变异，但可能尚未准备好进行个人级别的安全监测。