Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech
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{"title"=>"Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech", "type"=>"journal", "authors"=>[{"first_name"=>"Edward", "last_name"=>"Hill", "scopus_author_id"=>"53463543900"}, {"first_name"=>"David", "last_name"=>"Han", "scopus_author_id"=>"54882790500"}, {"first_name"=>"Pierre", "last_name"=>"Dumouchel", "scopus_author_id"=>"6603911203"}, {"first_name"=>"Najim", "last_name"=>"Dehak", "scopus_author_id"=>"16202714800"}, {"first_name"=>"Thomas", "last_name"=>"Quatieri", "scopus_author_id"=>"7005856167"}, {"first_name"=>"Charles", "last_name"=>"Moehs", "scopus_author_id"=>"53463974800"}, {"first_name"=>"Marlene", "last_name"=>"Oscar-Berman", "scopus_author_id"=>"7004833851"}, {"first_name"=>"John", "last_name"=>"Giordano", "scopus_author_id"=>"35147887200"}, {"first_name"=>"Thomas", "last_name"=>"Simpatico", "scopus_author_id"=>"6505956902"}, {"first_name"=>"Kenneth", "last_name"=>"Blum", "scopus_author_id"=>"35433478500"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"369287235", "doi"=>"10.1371/journal.pone.0069043", "sgr"=>"84879973823", "scopus"=>"2-s2.0-84879973823", "isbn"=>"1932-6203", "pmid"=>"23874860"}, "id"=>"04b4b79e-15a5-3d4e-99c2-7746259bfd70", "abstract"=>"Addictions to illicit drugs are among the nation's most critical public health and societal problems. The current opioid prescription epidemic and the need for buprenorphine/naloxone (Suboxone®; SUBX) as an opioid maintenance substance, and its growing street diversion provided impetus to determine affective states (\"true ground emotionality\") in long-term SUBX patients. Toward the goal of effective monitoring, we utilized emotion-detection in speech as a measure of \"true\" emotionality in 36 SUBX patients compared to 44 individuals from the general population (GP) and 33 members of Alcoholics Anonymous (AA). Other less objective studies have investigated emotional reactivity of heroin, methadone and opioid abstinent patients. These studies indicate that current opioid users have abnormal emotional experience, characterized by heightened response to unpleasant stimuli and blunted response to pleasant stimuli. However, this is the first study to our knowledge to evaluate \"true ground\" emotionality in long-term buprenorphine/naloxone combination (Suboxone™). We found in long-term SUBX patients a significantly flat affect (p<0.01), and they had less self-awareness of being happy, sad, and anxious compared to both the GP and AA groups. We caution definitive interpretation of these seemingly important results until we compare the emotional reactivity of an opioid abstinent control using automatic detection in speech. These findings encourage continued research strategies in SUBX patients to target the specific brain regions responsible for relapse prevention of opioid addiction.", "link"=>"http://www.mendeley.com/research/long-term-suboxone-emotional-reactivity-measured-automatic-detection-speech", "reader_count"=>17, "reader_count_by_academic_status"=>{"Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>2, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>2, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>2, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>3, "Medicine and Dentistry"=>3, "Psychology"=>3, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>2, "Indonesia"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1115382"], "description"=>"<p>The activation dimension (a.k.a. arousal dimension) refers to the degree of intensity (loudness, energy) in the emotional speech; and the evaluation dimension refers to how positive or negative the emotion is perceived <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Tato1\" target=\"_blank\">[37]</a>. Emotional states with high and low level of arousal are hardly ever confused, but it is difficult to determine the emotion of a person with flat affect <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Arnott1\" target=\"_blank\">[36]</a>. Emotions that are close in the activation-evaluation emotional space (flat affect) often tend to be confused <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Tato1\" target=\"_blank\">[37]</a>.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy"], "article_id"=>742752, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g005", "stats"=>{"downloads"=>3, "page_views"=>64, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Activation_Evaluation_Emotional_space_/742752", "title"=>"Activation-Evaluation Emotional space.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115380"], "description"=>"<p>Trial data capture is multilevel with emotional state samples grouped within patients. Frequencies of samples per patient are skewed towards a Poisson distribution; typical of ESM data collections. The mean is 64.4 and the median is 36.5 momentary emotional states per patient. On average participants answered 41% of emotion collection calls. SUBX patients answered significantly fewer calls (18.6%) as compared to the General Population (56.4%) and members of Alcoholics Anonymous (49.3%).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "states"], "article_id"=>742750, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Frequency_of_emotional_states_collected_per_participant_/742750", "title"=>"Frequency of emotional states collected per participant.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115394"], "description"=>"<p>Gender and language of the research participants.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy"], "article_id"=>742764, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gender_and_language_of_the_research_participants_/742764", "title"=>"Gender and language of the research participants.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115391"], "description"=>"<p>This figure captures daily call rates for an actual SUBX patient. The Y axis indicates the number of calls made to a patient per day (X axis). Successful calls, where a momentary emotional state is registered, are represented in light grey. Unsuccessful calls, where there was no answer or the answering machine responded, are represented in dark grey. There is a period from 2011-03-11 (March 11, 2011) thru to 2011-03-15 where the SUBX patient did not answer their phone. In the worst case this could be an indication of relapse or isolation due to depression. In the best case this could coincide with the patient being away from their home or cell phone, or simply apathy towards the IVR system. In a clinical implementation, a notification could be automatically sent to the therapist or case worker for follow-up.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "missed"], "article_id"=>742761, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g013", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SUBX_patient_call_rate_with_period_of_missed_calls_/742761", "title"=>"SUBX patient call rate with period of missed calls.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115389"], "description"=>"<p>Interestingly, and as can be seen in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone-0069043-g011\" target=\"_blank\">Figure 11</a>, the SUBX patients were more empathic to the neutral emotion state (76.5%; CI: 72.3–80.2) than AA members (p = 0.022) (71.7%; CI: 68.9–74.3). AA members were less empathic to anxiety (90.4%; CI: 86.7–93.1) than the GP (p = 0.022) (93.5%; CI: 91.8–94.8) and SUBX patients (p = 0.048) (93.5%; CI: 90.3–95.7).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "differences", "empathy"], "article_id"=>742759, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g011", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_differences_in_emotional_empathy_across_groups_/742759", "title"=>"Significant differences in emotional empathy across groups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115390"], "description"=>"<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone-0069043-g012\" target=\"_blank\">Figure 12</a> shows that the SUBX group had significantly less emotional expressiveness, as measured by length of speech, than both the GP group and the AA group (p<0.0001). It may be difficult to determine the emotion of SUBX patients, both by humans and by the automatic detector, due to flatter affect. The average audio response to “How are you feeling?” was (3.07 seconds; CI: 2.89–3.25). SUBX patients’ responses were significantly shorter (2.39 seconds; CI: 2.05–2.78)) than both the GP (p<0.0001) (3.46; CI: 3.15–2.80) and AA members (p<0.0001) (3.31; CI: 2.97–3.68). In terms of emotional expressiveness as measured by confidence scores, the SUBX group also showed significantly lower scores than both the GP and the AA groups. There was significantly less confidence in SUBX patients’ audio responses (72%; CI: 0.69–0.74) than the GP (p = 0.038) (74%; CI: 0.73–0.76) and AA members (p = 0.018) (75%; CI: 0.73–0.77).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "differences", "expressiveness"], "article_id"=>742760, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g012", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_differences_in_emotional_expressiveness_across_groups_/742760", "title"=>"Significant differences in emotional expressiveness across groups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115388"], "description"=>"<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone-0069043-g010\" target=\"_blank\">Figure 10</a> shows that the SUBX patients tended to be less self-aware of happiness (75.3%; CI:68.4–81.1) than the GP group (p = 0.066) (78.8%; CI: 76.6–80.8); less self-aware of sadness (85.3%; CI:78.3–90.3) than AA members (p = 0.013) (91.3%; CI: 87.4–93.6) and tended to be less self-aware of sadness than the GP (p = 0.082) (89.6%; CI: 87.8–91.2); less self-aware of their neutral state (63.2%; CI:57.0–69.0) than the GP (p = 0.008) (70.7%; CI:67.3–74.0) and AA members (p = 0.022) (71.7%; CI: 68.9–74.3); and less self-aware of their anxiety (91.8%; CI: 85.8–95.4) than the GP (p = 0.033) (95.8%; CI:93.4–97.1) and AA members (p = 0.022) (95.6%; CI:93.4–97.1).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "differences", "self-awareness"], "article_id"=>742758, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g010", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_differences_in_emotional_self_awareness_across_groups_/742758", "title"=>"Significant differences in emotional self-awareness across groups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115384"], "description"=>"<p>The overall accuracy of the speech emotion detector is 62.58% (95% CI: 61.5%–63.6%) The concordance matrix of predicted values from the emotion detector versus the labeled emotion is presented on the left of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone-0069043-g007\" target=\"_blank\">figure 7</a>. The Diagonal provides the accuracy of each emotional class (predicted emotion = actual emotion). Off-diagonal cells give percentages of false recognition (e.g. anxious accuracy was 72%, with 14% anxious recordings falsely categorized as okay or neutral, 8% falsely categorized as happy, 4% falsely categorized as sad, and 2% falsely categorized as angry). The heat map on the right graphically depicts the concordance matrix with correct predictions on the diagonal (predicted class is flipped upside down).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "detector"], "article_id"=>742754, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g007", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automatic_emotion_detector_results_/742754", "title"=>"Automatic emotion detector results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115379"], "description"=>"<p>The Voice User Interface (VUI) dialogue was carefully crafted to (1) capture a patient’s emotional expression, emotional self-assessment, and empathic assessment of another human’s emotional expression; and (2) to avoid subject-burden and training. The average call length is 12 seconds thus alleviating subject-burden (post collection surveys indicate ease-of-use. Call completion rates were 40% (95% CI: 33.6–46.7) (p = 0.003). Emotional expression in speech is elicited by asking the quintessential question “how do you feel?” It is human nature to colour our response to this question with emotion <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Scott1\" target=\"_blank\">[27]</a>. Emotional self-assessment is captured by asking the patient to identify their emotional state from the emotion set: (Neutral, Happy, Sad, Angry and Anxious) by selecting the corresponding choice on their DTMF telephone keypad. The system captures empathy by prompting the patient with: “guess the emotion of the following speaker” followed by the playback of a randomly selected previously captured speech recording from another patient. The patient listens to the emotionally charged speech recording and registers an empathy assessment by selecting the corresponding choice from the emotion set on their DTMF telephone keypad.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "interactive"], "article_id"=>742749, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g002", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_Interactive_Voice_Response_dialogue_/742749", "title"=>"An Interactive Voice Response dialogue.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115393"], "description"=>"<p>Example of calculation of emotion from four sources.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy"], "article_id"=>742763, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.t003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_calculation_of_emotion_from_four_sources_/742763", "title"=>"Example of calculation of emotion from four sources.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115378"], "description"=>"<p>Patient-reported-outcome (PRO) Experience Sampling Method (ESM) data collection places considerable demands on participants. Success of an ESM data collection depends upon participant compliance with the sampling protocol. Participants must record an ESM at least 20% of the time when requested to do so; otherwise the validity of the protocol is questionable. The problem of “hoarding” – where reports are collected and completed at a later date – must be avoided. Stone et al confirmed this concern through a study and found only 11% of pen-and-pencil diaries where compliant; 89% of participants missed entries, or hoarded entries and bulk entered them later. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Stone2\" target=\"_blank\">[58]</a> IVR systems overcome hoarding by time-sampling and improve compliance by allowing researchers to actively place outgoing calls to participants in order to more dynamically sample their experience. Rates of compliance in IVR sampling literature vary from as high as 96% to as low as 40% <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Hufford1\" target=\"_blank\">[59]</a> Subject burden has also been studied as a factor effecting compliance rates. At least six different aspects affect participant burden: Density of sampling (times per day); length of PRO assessments; the user interface of the reporting platform; the complexity of PRO assessments (i.e. the cognitive load, or effort, required to complete the assessments); duration of monitoring; and stability of the reporting platform <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Hufford1\" target=\"_blank\">[59]</a>. Researchers have been known to improve compliance through extensive training of participants <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone.0069043-Stone2\" target=\"_blank\">[58]</a>. Extensive training is impractical for automated ESM systems. Patients were called by the IVR system at designated times thus overcoming hoarding. A simple intuitive prompt: “How are you feeling?” elicited emotional state response (e.g., “I am angry!”); no training was required. The audio response is recorded on the web server for analysis. The IVR system was implemented through the W3C standards CCXML and VoiceXML on a Linux-Apache-MySQL-PHP (LAMP) server cluster.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "momentary", "interactive"], "article_id"=>742748, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g001", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patient_Momentary_Emotional_State_collection_through_the_Interactive_Voice_Response_system_/742748", "title"=>"Patient Momentary Emotional State collection through the Interactive Voice Response system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115392"], "description"=>"<p>Example of majority-vote sources.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "majority-vote"], "article_id"=>742762, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_majority_vote_sources_/742762", "title"=>"Example of majority-vote sources.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115387"], "description"=>"<p>Statistical analyses of emotions showed that SUBX patients had a lower probability of being happy (15.2%; CI: 9.7–22.9) than both the GP (p = 0.0171) (24.7%; CI: 19.2–31.0) and AA groups (p = 0.031) (24.0%; CI: 18.2–31.0). However, AA members had over twice the probability of being anxious (4.8%; CI: 3.2–7.3) than SUBX patients (p = 0.028) (2.2%; CI: 1.1–4.5).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "differences"], "article_id"=>742757, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g009", "stats"=>{"downloads"=>7, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_emotion_differences_across_groups_/742757", "title"=>"Significant emotion differences across groups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115386"], "description"=>"<p>Naïve Bayes with equal emotion class weights is used to calculate the maximum likelihood that an utterance X corresponds to the emotion e. In this example the automatic emotion detector classified the speech recording as Happy, with a likelihood estimation (score) of 0 (the higher the score, the more likely the classification).</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "detection"], "article_id"=>742756, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g008", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_automatic_emotion_detection_likelihood_estimation_/742756", "title"=>"Example of automatic emotion detection likelihood estimation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115383"], "description"=>"<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069043#pone-0069043-g006\" target=\"_blank\">Figure 6</a> depicts the model training and detection stages of the emotion detector. Models are trained in the left pane of the figure. Emotion detection classification computes the most likely emotion using the trained models as shown in the right pane. Speech Activity Detection and Feature Extraction are identical in both model training and classification.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "stages"], "article_id"=>742753, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g006", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_stages_of_emotion_detection_model_training_and_real_time_detection_/742753", "title"=>"Two stages of emotion detection: model training and real time detection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1115381"], "description"=>"<p>Speech duration of patients’ emotional expression in response to “How are you feeling?” shows that 75% of speech captured was less than 4.6 seconds.The mean is 3.79 seconds and the median is 2.97 seconds. Some utterances (e.g. “ok”) are as short as 0.1 seconds, Minimum and maximum speech durations influenced the design of the speech activity detector.</p>", "links"=>[], "tags"=>["Computer applications", "Computing systems", "software engineering", "Mental health", "psychology", "therapies", "Public health", "alcohol", "Behavioral and social aspects of health", "Drug policy", "duration"], "article_id"=>742751, "categories"=>["Information And Computing Sciences", "Medicine"], "users"=>["Edward Hill", "David Han", "Pierre Dumouchel", "Najim Dehak", "Thomas Quatieri", "Charles Moehs", "Marlene Oscar-Berman", "John Giordano", "Thomas Simpatico", "Kenneth Blum"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069043.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Speech_duration_of_emotional_responses_/742751", "title"=>"Speech duration of emotional responses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-09 03:14:31"}

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Relative Metric

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