Affordable, speedy and intelligent psychotherapy assessment.

Lyssn uses machine learning to evaluate psychotherapy sessions and generates performance-based feedback.

How Lyssn Works

Speaker Identification

Speech Recognition

Lyssn uses speech recognition to recognize words and sentences. Our technology also detects emotional tone, offering richer cues to help evaluate sessions.


Speaker Identification

In psychotherapy, there is more than one speaker. Lyssn differentiates between therapists and clients (“who speaks when?”). We partition the transcript into speaker-labeled talk turns for easier analysis.



Lyssn uses state-of-the-art machine learning technology to identify key session content, including specific therapist interventions, discussion topics, and emotions.



Lyssn provides an interactive tool for reviewing and annotating sessions, including high-level session summaries and detailed information contained within session transcripts.

Why Lyssn?


Feedback is instant.

Until now, feedback on the quality of psychotherapy relied on burdening patients with questionnaires, or slow and labor-intensive human evaluations of session recordings. Lyssn utilizes state-of-the-art machine learning technology, trained on thousands of human-evaluated sessions, to process the content of your session in real time.


Training, supervision, quality assurance.

Lyssn performance-based feedback is designed for therapist's own reflection and growth, training in specific evidence-based practices and skills, and to deepen and facilitate the process of supervision. Lyssn is currently developing technology to provide feedback on the quality and content of psychotherapy to patient's themselves


User-centered design

Learning from your work should be easy. Our designers worked directly with therapists and trainers to develop an easy-to-use, elegant platform so that you not only receive feedback instantly but in a way you can understand.


Lyssn cares about your privacy.

What clients share with their therapists is private, and we take security seriously. Our infrastructure utilizes two-factor authentication and encrypted, HIPAA-compliant cloud servers.

Backed by Years of Scientific Research

Imel et al. (2015)

  • Imel, Zac
  • Steyvers, Mark
  • Atkins, David

Computational psychotherapy research: Scaling up the evaluation of patient provider interactions


Xiao et al. (2015)

  • Xiao, Bo
  • Imel, Zac
  • Georgiou, Panayiotis


"Rate my therapist": Automated detection of empathy in drug and alcohol counseling


Hirsch et al. (2017)

  • Hirsch, Tad
  • Merced, Kritzia
  • Imel, Zac


Designing Contestability: Interaction Design, Machine Learning, and Mental Health.

ACM Conference on Designing Interactive Systems

Narayanan et al. (2013)

  • Narayanan, Shrikanath
  • Georgiou, Panayiotis

Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language

Proceedings of the IEEE

Tanana et al. (2016)

  • Tanana, Michael
  • Hallgren, Kevin
  • Imel, Zac


A comparison of natural language processing methods for automated coding of motivational interviewing

Journal of Substance Abuse Treatment

Contact Us

Dave Atkins

Chief Executive Officer

Tad Hirsch

Chief Design Officer

Shri Narayanan

Chief Science Officer

Zac Imel

Chief Innovation Officer

Mike Tanana

Chief Technology Officer