We describe an experiment conducted with three domain experts to understand how well they can recognize types and performance stages of activities using speech data transcribed from verbal communications during dynamic medical teamwork. The insights gained from this experiment will inform the design of an automatic activity recognition system to alert medical teams to process deviations in real time. We contribute to the literature by (1) characterizing how domain experts perceive the dynamics of activity-related speech, and (2) identifying the challenges associated with system design for speech-based activity recognition in complex team-based work settings.
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A Speech-Based Model for Tracking the Progression of Activities in Extreme Action Teamwork
Designing computerized approaches to support complex teamwork requires an understanding of how activity-related information is relayed among team members. In this paper, we focus on verbal communication and describe a speech-based model that we developed for tracking activity progression during time-critical teamwork. We situated our study in the emergency medical domain of trauma resuscitation and transcribed speech from 104 audio recordings of actual resuscitations. Using the transcripts, we first studied the nature of speech during 34 clinically relevant activities. From this analysis, we identified 11 communicative events across three different stages of activity performance-before, during, and after. For each activity, we created sequential ordering of the communicative events using the concept of narrative schemas. The final speech-based model emerged by extracting and aggregating generalized aspects of the 34 schemas. We evaluated the model performance by using 17 new transcripts and found that the model reliably recognized an activity stage in 98% of activity-related conversation instances. We conclude by discussing these results, their implications for designing computerized approaches that support complex teamwork, and their generalizability to other safety-critical domains.
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- Award ID(s):
- 1763509
- PAR ID:
- 10379017
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 6
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In clinical settings, most automatic recognition systems use visual or sensory data to recognize activities. These systems cannot recognize activities that rely on verbal assessment, lack visual cues, or do not use medical devices. We examined speech-based activity and activity-stage recognition in a clinical domain, making the following contributions. (1) We collected a high-quality dataset representing common activities and activity stages during actual trauma resuscitation events-the initial evaluation and treatment of critically injured patients. (2) We introduced a novel multimodal network based on audio signal and a set of keywords that does not require a high-performing automatic speech recognition (ASR) engine. (3) We designed novel contextual modules to capture dynamic dependencies in team conversations about activities and stages during a complex workflow. (4) We introduced a data augmentation method, which simulates team communication by combining selected utterances and their audio clips, and showed that this method contributed to performance improvement in our data-limited scenario. In offline experiments, our proposed context-aware multimodal model achieved F1-scores of 73.2±0.8% and 78.1±1.1% for activity and activity-stage recognition, respectively. In online experiments, the performance declined about 10% for both recognition types when using utterance-level segmentation of the ASR output. The performance declined about 15% when we omitted the utterance-level segmentation. Our experiments showed the feasibility of speech-based activity and activity-stage recognition during dynamic clinical events.
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We describe an analysis of speech during time-critical, team-based medical work and its potential to indicate process delays. We analyzed speech intention and sentence types during 39 trauma resuscitations with delays in one of three major lifesaving interventions: intravenous/intraosseous (IV/IO) line insertion, cardiopulmonary and resuscitation (CPR), and intubation. We found a significant difference in patterns of speech during delays vs. speech during non-delayed work. The speech intention during CPR delays, however, differed from the other LSIs, suggesting that context of speech must be considered. These findings will inform the design of a clinical decision support system (CDSS) that will use multiple sensor modalities to alert medical teams to delays in real time. We conclude with design implications and challenges associated with speech-based activity recognition in complex medical processes.more » « less
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We describe an analysis of speech during time-critical, team-based medical work and its potential to indicate process delays. We analyzed speech intention and sentence types during 39 trauma resuscitations with delays in one of three major lifesaving interventions: intravenous/intraosseous (IV/IO) line insertion, cardiopulmonary and resuscitation (CPR), and intubation. We found a significant difference in patterns of speech during delays vs. speech during non-delayed work. The speech intention during CPR delays, however, differed from the other LSIs, suggesting that context of speech must be considered. These findings will inform the design of a clinical decision support system (CDSS) that will use multiple sensor modalities to alert medical teams to delays in real time. We conclude with design implications and challenges associated with speech-based activity recognition in complex medical processes.more » « less
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End-to-end spoken language understanding (SLU) systems are typically trained on large amounts of data. In many practical scenarios, the amount of labeled speech is often limited as opposed to text. In this study, we investigate the use of non-parallel speech and text to improve the performance of dialog act recognition as an example SLU task. We propose a multiview architecture that can handle each modality separately. To effectively train on such data, this model enforces the internal speech and text encodings to be similar using a shared classifier. On the Switchboard Dialog Act corpus, we show that pretraining the classifier using large amounts of text helps learning better speech encodings, resulting in up to 40% relatively higher classification accuracies. We also show that when the speech embeddings from an automatic speech recognition (ASR) system are used in this framework, the speech-only accuracy exceeds the performance of ASR-text based tests up to 15% relative and approaches the performance of using true transcripts.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3512920
