125 research outputs found
A Study on Prosodic Entrainment in Relation to Therapist Empathy in Counseling Conversation
Counseling is carried out as spoken conversation between a therapist and a
client. The empathy level expressed by the therapist is considered an important
index of the quality of counseling and often assessed by an observer or the
client. This research investigates the entrainment of speech prosody in
relation to subjectively rated empathy. Experimental results show that the
entrainment of intensity is more influential to empathy observation than that
of pitch or speech rate in client-therapist interaction. The observer and the
client have different perceptions of therapist empathy with the same entrained
phenomena in pitch and intensity. The client's intention to make adjustment on
pitch variation and intensity of speech is considered an indicator of the
client's perception of counseling quality.Comment: Accepted by INTERSPEECH 202
Hierarchical Attention Network for Evaluating Therapist Empathy in Counseling Session
Counseling typically takes the form of spoken conversation between a
therapist and a client. The empathy level expressed by the therapist is
considered to be an essential quality factor of counseling outcome. This paper
proposes a hierarchical recurrent network combined with two-level attention
mechanisms to determine the therapist's empathy level solely from the acoustic
features of conversational speech in a counseling session. The experimental
results show that the proposed model can achieve an accuracy of 72.1% in
classifying the therapist's empathy level as being "high" or "low". It is found
that the speech from both the therapist and the client are contributing to
predicting the empathy level that is subjectively rated by an expert observer.
By analyzing speaker turns assigned with high attention weights, it is observed
that 2 to 6 consecutive turns should be considered together to provide useful
clues for detecting empathy, and the observer tends to take the whole session
into consideration when rating the therapist empathy, instead of relying on a
few specific speaker turns.Comment: Submitted to INTERSPEECH 202
A speech-based empathy training system - initial design insights
Empathy is an essential component of human communication since it increases our understanding and perception of others. However, studies show that students\u27 empathy skills have declined rapidly in the last decades. Against this background, practitioner reports predict that the importance of empathy will increase as a skill for successful agile teamwork in the future. Therefore, researchers have designed information systems to train empathy abilities of learners in different domains. Nevertheless, research on automated speech-based training is rather scarce. Hence, we aim to investigate how to design a speech-based empathy training system that helps students react emotionally adequately in communication. This research in progress paper presents five initial requirements that guide future research and development of a speech-based empathy training system intended to support students\u27 self-regulated learning. With this, we hope to provide guidance for the design and embedding of speech-based empathy training systems at scale
Natural Language Processing for Motivational Interviewing Counselling: Addressing Challenges in Resources, Benchmarking and Evaluation
Motivational interviewing (MI) is a counselling style often used in healthcare to improve patient health and quality of life by promoting positive behaviour changes. Natural language processing (NLP) has been explored for supporting MI use cases of insights/feedback generation and therapist training, such as automatically assigning behaviour labels to therapist/client utterances and generating possible therapist responses.
Despite the progress of NLP for MI applications, significant challenges remain. The most prominent one is the lack of publicly available and annotated MI dialogue corpora due to privacy constraints. Consequently, there is also a lack of common benchmarks and poor reproducibility across studies. Furthermore, human evaluation for therapist response generation is expensive and difficult to scale due to its dependence on MI experts as evaluators. In this thesis, we address these challenges in 4 directions: low-resource NLP modelling, MI dialogue dataset creation, benchmark development for real-world applicable tasks, and laypeople-experts human evaluation study.
First, we explore zero-shot binary empathy assessment at the utterance level. We experiment with a supervised approach that trains on heuristically constructed empathy vs. non-empathy contrast in non-therapy dialogues. While this approach has better performance than other models without empathy-aware training, it is still suboptimal and therefore highlights the need for a well-annotated MI dataset.
Next, we create AnnoMI, the first publicly available dataset of expert-annotated MI dialogues. It contains MI conversations that demonstrate both high- and low-quality counselling, with extensive annotations by domain experts covering key MI attributes. We also conduct comprehensive analyses of the dataset.
Then, we investigate two AnnoMI-based real-world applicable tasks: predicting current-turn therapist/client behaviour given the utterance, and forecasting next-turn therapist behaviour given the dialogue history. We find that language models (LMs) perform well on predicting therapist behaviours with good generalisability to new dialogue topics. However, LMs have suboptimal forecasting performance, which reflects therapists' flexibility where multiple optimal next-turn actions are possible.
Lastly, we ask both laypeople and experts to evaluate the generation of a crucial type of therapist responses -- reflection -- on a key quality aspect: coherence and context-consistency. We find that laypeople are a viable alternative to experts, as laypeople show good agreement with each other and correlation with experts. We also find that a large LM generates mostly coherent and consistent reflections.
Overall, the work of this thesis broadens access to NLP for MI significantly as well as presents a wide range of findings on related natural language understanding/generation tasks with a real-world focus. Thus, our contributions lay the groundwork for the broader NLP community to be more engaged in research for MI, which will ultimately improve the quality of life for recipients of MI counselling
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