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Neural Speech Decoding During Audition, Imagination and Production
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In: IEEE (2021)
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A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networks ...
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Vocal tract shaping of emotional speech
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In: Comput Speech Lang (2020)
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Data from: Speed-accuracy tradeoffs in human speech production ...
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Dynamic Off-resonance Correction for Spiral Real-Time MRI of Speech
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A technology prototype system for rating therapist empathy from audio recordings in addiction counseling
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Advances in real-time magnetic resonance imaging of the vocal tract for speech science and technology research
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"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing
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Abstract:
The technology for evaluating patient-provider interactions in psychotherapy–observational coding–has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies.
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Keyword:
Research Article
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URL: https://doi.org/10.1371/journal.pone.0143055 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668058/ http://www.ncbi.nlm.nih.gov/pubmed/26630392
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On Quantifying Facial Expression-Related Atypicality of Children with Autism Spectrum Disorder
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Directly data-derived articulatory gesture-like representations retain discriminatory information about phone categories
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Dynamic 3-D visualization of vocal tract shaping during speech
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Analyzing the Language of Therapist Empathy in Motivational Interview based Psychotherapy
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