"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing
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{"title"=>"\"Rate My Therapist\": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing", "type"=>"journal", "authors"=>[{"last_name"=>"Bo Xiao", "scopus_author_id"=>"57076927300"}, {"first_name"=>"Zac E.", "last_name"=>"Imel", "scopus_author_id"=>"13405510700"}, {"first_name"=>"Panayiotis G.", "last_name"=>"Georgiou", "scopus_author_id"=>"7003719827"}, {"first_name"=>"David C.", "last_name"=>"Atkins", "scopus_author_id"=>"7102290550"}, {"first_name"=>"Shrikanth S.", "last_name"=>"Narayanan", "scopus_author_id"=>"11539478500"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84955508891", "sgr"=>"84955508891", "pui"=>"607883997", "isbn"=>"1932-6203", "pmid"=>"26630392", "doi"=>"10.1371/journal.pone.0143055"}, "id"=>"20883af5-2b63-3761-8cab-b65eb38f642e", "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.", "link"=>"http://www.mendeley.com/research/rate-therapist-automated-detection-empathy-drug-alcohol-counseling-via-speech-language-processing", "reader_count"=>40, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Librarian"=>1, "Student > Doctoral Student"=>5, "Researcher"=>8, "Student > Ph. D. Student"=>9, "Student > Master"=>6, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Librarian"=>1, "Student > Doctoral Student"=>5, "Researcher"=>8, "Student > Ph. D. Student"=>9, "Student > Master"=>6, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Nursing and Health Professions"=>2, "Medicine and Dentistry"=>3, "Business, Management and Accounting"=>1, "Psychology"=>16, "Social Sciences"=>1, "Computer Science"=>12}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>16}, "Computer Science"=>{"Computer Science"=>12}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Australia"=>2, "Spain"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2598554"], "description"=>"<p>The lower portion of the figure represents the process for a single session recording, whereas the upper portion represents various speech signal processing tasks, learned from all available corpora (as indicated in the text).</p>", "links"=>[], "tags"=>["rating", "speech processing tasks", "prediction", "provider", "evaluation", "empathy", "session recordings", "classification", "asr", "language processing methods", "psychotherapy", "MI training methods", "technology", "automatic speech recognition", "transcript", "200 Motivational Interviewing"], "article_id"=>1617393, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Xiao", "Zac E. Imel", "Panayiotis G. Georgiou", "David C. Atkins", "Shrikanth S. Narayanan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143055.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_processing_steps_for_moving_from_audio_recording_of_session_to_predicted_value_of_empathy_/1617393", "title"=>"Overview of processing steps for moving from audio recording of session to predicted value of empathy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-02 04:58:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/2598555"], "description"=>"<p><i>Note</i>. ASR = Automatic speech recognition; LM = language model; AM = acoustic model</p><p><sup>a</sup> For further information on the specific MI randomized trials are summarized see. [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref024\" target=\"_blank\">24</a>] Specific studies include Alcohol Research Collaborative: Peer Programs; [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref052\" target=\"_blank\">52</a>] Event Specific Prevention: Spring Break; [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref053\" target=\"_blank\">53</a>] Event Specific Prevention: Twenty First Birthday; [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref054\" target=\"_blank\">54</a>] Brief Intervention for Problem Drug Use and Abuse in Primary Care; [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref055\" target=\"_blank\">55</a>] Indicated Marijuana Prevention for Frequently Using College Students. [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref056\" target=\"_blank\">56</a>]</p><p><sup>b</sup> CTT = Context Tailored Training. [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143055#pone.0143055.ref023\" target=\"_blank\">23</a>]</p><p>Summary of psychotherapy corpora and role in automatic empathy evaluation.</p>", "links"=>[], "tags"=>["rating", "speech processing tasks", "prediction", "provider", "evaluation", "empathy", "session recordings", "classification", "asr", "language processing methods", "psychotherapy", "MI training methods", "technology", "automatic speech recognition", "transcript", "200 Motivational Interviewing"], "article_id"=>1617394, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Xiao", "Zac E. Imel", "Panayiotis G. Georgiou", "David C. Atkins", "Shrikanth S. Narayanan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143055.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_psychotherapy_corpora_and_role_in_automatic_empathy_evaluation_/1617394", "title"=>"Summary of psychotherapy corpora and role in automatic empathy evaluation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-02 04:58:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/2598556"], "description"=>"<p><sup>a</sup> These results were calculated on 63 sessions instead of 200. On sessions coded by multiple coders, the average opinion is used to derive the dichotomous (binarized) decision. The opinion of the individual coder is compared with the average decision to establish this coder-agreement.</p><p><sup>b</sup> Result is fully automatic, no human intervention in algorithm.</p><p>Empathy prediction performance.</p>", "links"=>[], "tags"=>["rating", "speech processing tasks", "prediction", "provider", "evaluation", "empathy", "session recordings", "classification", "asr", "language processing methods", "psychotherapy", "MI training methods", "technology", "automatic speech recognition", "transcript", "200 Motivational Interviewing"], "article_id"=>1617395, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Xiao", "Zac E. Imel", "Panayiotis G. Georgiou", "David C. Atkins", "Shrikanth S. Narayanan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143055.t002", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empathy_prediction_performance_/1617395", "title"=>"Empathy prediction performance.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-02 04:58:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/2598557"], "description"=>"<p>High vs. low empathy tri-grams.</p>", "links"=>[], "tags"=>["rating", "speech processing tasks", "prediction", "provider", "evaluation", "empathy", "session recordings", "classification", "asr", "language processing methods", "psychotherapy", "MI training methods", "technology", "automatic speech recognition", "transcript", "200 Motivational Interviewing"], "article_id"=>1617396, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Xiao", "Zac E. Imel", "Panayiotis G. Georgiou", "David C. Atkins", "Shrikanth S. Narayanan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143055.t003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_High_vs_low_empathy_tri_grams_/1617396", "title"=>"High vs. low empathy tri-grams.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-02 04:58:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/2598558"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["rating", "speech processing tasks", "prediction", "provider", "evaluation", "empathy", "session recordings", "classification", "asr", "language processing methods", "psychotherapy", "MI training methods", "technology", "automatic speech recognition", "transcript", "200 Motivational Interviewing"], "article_id"=>1617397, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Xiao", "Zac E. Imel", "Panayiotis G. Georgiou", "David C. Atkins", "Shrikanth S. Narayanan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143055", "stats"=>{"downloads"=>5, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rate_My_Therapist_Automated_Detection_of_Empathy_in_Drug_and_Alcohol_Counseling_via_Speech_and_Language_Processing_/1617397", "title"=>"\"Rate My Therapist\": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-02 04:58:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/2598551"], "description"=>"<p>Distribution of human empathy ratings for the 200 sessions in the CTT trial.</p>", "links"=>[], "tags"=>["rating", "speech processing tasks", "prediction", "provider", "evaluation", "empathy", "session recordings", "classification", "asr", "language processing methods", "psychotherapy", "MI training methods", "technology", "automatic speech recognition", "transcript", "200 Motivational Interviewing"], "article_id"=>1617390, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Bo Xiao", "Zac E. Imel", "Panayiotis G. Georgiou", "David C. Atkins", "Shrikanth S. Narayanan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143055.g001", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_human_empathy_ratings_for_the_200_sessions_in_the_CTT_trial_/1617390", "title"=>"Distribution of human empathy ratings for the 200 sessions in the CTT trial.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-02 04:58:24"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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