Data
Capture Using Spoken Dialog
This project investigates the use of spoken dialog system as a tool for real-time data collection for healthcare, life and behavioral science. Specifically, we implemented a dialog system, Pain Monitoring Voice Diary, for monitoring chronic pain patients.
Task Characteristics:
The characteristics and requirements of data capture task are different than those for other applications of spoken dialog technology. Successful dialog design needs to take the following specificities of this task into account:
1. The subjects participating in data collection are enrolled through a personal face-to face interview at which they receive relevant information about the trial and guidance on the process of data collection. In the same opportunity the patients can receive some training, explanation and possibly a demo on how to use the spoken dialog system.
2. Subjects call the system repeatedly according to the study protocol, and identify themselves in the beginning of the session. This provides an opportunity to use the knowledge accumulated across sessions for personalization.
3. The system should accommodate both novice callers (in the beginning of the trial) and experienced callers (those who completed several sessions); For the experienced caller, the system needs to provide short and effective call flow, without making the caller hear long and tedious prompts. For the novice caller, the system needs to provide enough information and help to guarantee question understanding and successful session completion.
4. Data validity, accuracy and integrity in this application are very important, since the penalty for an erroneously filed final session report can be very high. Since the automated speech recognition technology is not perfect, the design has to take into account the possibility of speech recognition errors and improve the overall accuracy using dialog actions such as re-prompts, confirmations, error handling, and, if necessary, recording and flagging the unrecognized utterances for later transcription.
In the design of the dialog we addressed these task characteristics by providing flexible level of user support, and controlling the captured data accuracy.
Usability
study:
Experimental evaluation of usability of the Pain Monitoring Voice Diary was performed with 24 volunteers. The goal of this evaluation was to prove the feasibility of data capture through dialog, and validate the assumptions underlying dialog design.
Publications
and Presentations:
E. Levin & A. Levin, Spoken
Dialog System for Real-Time Data Capture, in Proc. INTERSPEECH 2005,
September 4-8, 2005,
E. Levin & A. Levin, Automated Speech Recognition for Real-Time Data Collection, in
Critical Issues in eHealth Research Conference, June 2005,