JUPITER Data Collection and Analysis

We have been actively collecting data within the JUPITER domain since the beginning of 1997. As was done in previous domains, we first developed a prototype JUPITER system and used it to collect spontaneous speech using a Wizard paradigm, with a human typist in the loop and subjects brought into the lab and given scenarios to solve. At the same time, we solicited read speech using both our Web-based data collection facility and a phone number that subjects could call to read from pre-distributed lists. Once these data had been collected, we were able to train a recognizer and move on to system-based data collection. We currently have a toll-free number that is available 24-hours/day for subjects to call to find out weather information 1. The utterances collected from this facility are also used as training data. The toll-free number has been a particularly powerful method for collecting data from a variety of subjects in a short period of time. We feel that these calls accurately reflect the way users want to interact with such systems. In recent months, we have been receiving approximately 17 calls per day. In order to ensure that these data are ready to use as quickly as possible for both speech recognition and natural language development, we have been processing incoming data on a daily basis. Every morning a script automatically sends email containing a list of the previous days calls. These calls are then manually transcribed, usually over the course of the following day. The transcribed calls are then bundled into sets containing approximately 500 utterances and are added to the training corpus as they become available. Over the past year, we have continued to refine our transcription tool which was originally developed for orthographic transcription of read speech. We used a Tcl/ Tk interface to provide an editable window where the transcriber listen to utterances and correct existing transcriptions and add specialized markings for noise, partial words etc. The initial transcription for each utterance was the orthography hypothesized by the recognizer during the call. The transcriber could also identify the talker as male, female, or child using the transcription tool. The transcription tool uses a lexicon to check the quality of orthographic transcriptions. This feature is useful for finding typographical errors before they are saved. If a word does not appear in the lexicon, the transcriber is warned and given the option of …