Multi-label Multi-task Deep Learning for Behavioral Coding.

We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions is the annotated with labels to describe relevant human behaviors of interest. We describe two related, yet distinct, corpora consisting of therapist client interactions in psychotherapy sessions.

Confusion2Vec 2.0: Enriching ambiguous spoken language representations with subwords.

Word vector representations enable machines to encode human language for spoken language understanding and processing. Confusion2vec, motivated from human speech production and perception, is a word vector representation which encodes ambiguities present in human spoken language in addition to semantics and syntactic information. Confusion2vec provides a robust spoken language representation by considering inherent human language ambiguities.

Automated evaluation of psychotherapy skills using speech and language technologies.

With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is, however, a cost-prohibitive and time-consuming method that leads to poor feasibility and limited use in real-world settings.

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment.

Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations.

Children speech recognition is challenging mainly due to the inherent high variability in children's physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children's life. Part of the challenge is due to the lack of large amounts of available children speech data for efficient modeling.

Linking emotions to behaviors through deep transfer learning.

Human behavior refers to the way humans act and interact. Understanding human behavior is a cornerstone of observational practice, especially in psychotherapy. An important cue of behavior analysis is the dynamical changes of emotions during the conversation.

Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance.

Linguistic coordination is a well-established phenomenon in spoken conversations and often associated with positive social behaviors and outcomes. While there have been many attempts to measure lexical coordination or entrainment in literature, only a few have explored coordination in syntactic or semantic space. In this work, we attempt to combine these different aspects of coordination into a single measure by leveraging distances in a neural word representation space.

Unsupervised online multitask learning of behavioral sentence embeddings.

Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones.

Confusion2Vec: towards enriching vector space word representations with representational ambiguities.

Word vector representations are a crucial part of natural language processing (NLP) and human computer interaction. In this paper, we propose a novel word vector representation, Confusion2Vec, motivated from the human speech production and perception that encodes representational ambiguity. Humans employ both acoustic similarity cues and contextual cues to decode information and we focus on a model that incorporates both sources of information.

ROLE SPECIFIC LATTICE RESCORING FOR SPEAKER ROLE RECOGNITION FROM SPEECH RECOGNITION OUTPUTS.

The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding.

IMPROVING THE PREDICTION OF THERAPIST BEHAVIORS IN ADDICTION COUNSELING BY EXPLOITING CLASS CONFUSIONS.

In this work we address the problem of joint prosodic and lexical behavioral annotation for addiction counseling. We expand on past work that employed Recurrent Neural Networks (RNNs) on multimodal features by grouping and classifying subsets of classes. We propose two implementations: One is hierarchical classification, which uses the behavior confusion matrix to cluster similar classes and makes the prediction based on a tree structure.

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.

Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, this requires experts and training human coding teams, which is slow, expensive, and labor intensive. Machine learning and speech signal processing technologies provide a way to scale up feedback in psychotherapy.

Cardiovascular Reactivity During Marital Conflict in Laboratory and Naturalistic Settings: Differential Associations with Relationship and Individual Functioning Across Contexts.

Cardiovascular reactivity during spousal conflict is considered to be one of the main pathways for relationship distress to impact physical, mental, and relationship health. However, the magnitude of association between cardiovascular reactivity during laboratory marital conflict and relationship functioning is small and inconsistent given the scope of its importance in theoretical models of intimate relationships. This study tests the possibility that cardiovascular data collected in laboratory settings downwardly bias the magnitude of these associations when compared to measures obtained in naturalistic settings.

Conceptual and statistical issues in couples observational research: Rationale and methods for design decisions.

Observational behavioral coding methods are widely used for the study of relational phenomena. There are numerous guidelines for the development and implementation of these methods that include principles for creating new and adapting existing coding systems as well as principles for creating coding teams. While these principles have been successfully implemented in research on relational phenomena, the ever expanding array of phenomena being investigated with observational methods calls for a similar expansion of these principles.

Everyday couples' communication research: Overcoming methodological barriers with technology.

Relationship behaviors contribute to compromised health or resilience. Everyday communication between intimate partners represents the vast majority of their interactions. When intimate partners take on new roles as patients and caregivers, everyday communication takes on a new and important role in managing both the transition and the adaptation to the change in health status.

Predicting couple therapy outcomes based on speech acoustic features.

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes.

Multiple small intestinal perforations in a patient with Hepatitis B Virus-associated Polyarteritis Nodosa.

We present the case of a 38-year-old patient with a history of Hepatitis B Virus-associated Polyarteritis Nodosa, who presented with acute abdomen and septic shock. The patient initially had three perforations of the small intestine that were treated with segmental enterectomy and anastomosis at two sites. During his postoperative course he continued to develop new perforations and necrotic lesions along the whole length of the small intestine, that mandated repetitive laparotomies and the technique of the open abdomen was employed.

Clinical efficacy of omalizumab in chronic spontaneous urticaria is associated with a reduction of FcεRI-positive cells in the skin.

Treatment with omalizumab, a humanized recombinant monoclonal anti-IgE antibody, results in clinical efficacy in patients with Chronic Spontaneous Urticaria (CSU). The mechanism of action of omalizumab in CSU has not been elucidated in detail. To determine the effects of omalizumab on levels of high affinity IgE receptor-positive (FcεRI) and IgE-positive (IgE) dermal cells and blood basophils.

A technology prototype system for rating therapist empathy from audio recordings in addiction counseling.

Scaling up psychotherapy services such as for addiction counseling is a critical societal need. One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment. This work proposes a speech technology-based system to automate the assessment of therapist empathy-a key therapy quality index-from audio recordings of the psychotherapy interactions.

Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation.

"It sounds like...": A natural language processing approach to detecting counselor reflections in motivational interviewing.

The dissemination and evaluation of evidence-based behavioral treatments for substance abuse problems rely on the evaluation of counselor interventions. In Motivational Interviewing (MI), a treatment that directs the therapist to utilize a particular linguistic style, proficiency is assessed via behavioral coding-a time consuming, nontechnological approach. Natural language processing techniques have the potential to scale up the evaluation of behavioral treatments such as MI.