Enabling Information Retrieval from Conversational Speech Archives via Crowdsourcing
|This is an ongoing project.||
Contact DetailsMatt Lease
You will have the opportunity to contribute to active research in our lab. The lab typically includes 4-5 graduate students to interact with, and a shared lab space to optionally work in.
This project was featured in WIRED: http://www.wired.com/dangerroom/2013/03/darpa-speech
Advances in capture and storage technology now let us archive massive amounts of spontaneous (conversational) speech data. However, effective use of this data requires accurate information retrieval technology designed for and evaluated on spontaneous speech data. Unfortunately, traditional practice for benchmarking search engine accuracy cannot scale to “big data”, especialy for conversational speech. This restricts our ability to even measure the effectiveness of existing search engines, much less further advance them.
Evaluating search with spontaneous speech archives is particularly challenging vs. more traditional text collections. Unlike text, speech must first be transcribed, and while prepared speech (e.g. broadcast news) transcripts are very readable, spontaneous speech transcripts are often very difficult to read, even with perfect transcription, due to "disfluency" (self-corrections, trailing off, interruptions, etc.) and lack of commas and sentence boundaries. Human editing to correct this would require even greater manual effort. As a result, few spontaneous speech IR test collections exist today.
We are investigating use of nascent crowdsourcing (crowd computing) techniques in concert with "rich transcription" technology. While crowdsourcing offers tremendous potential for time and cost savings, how to achieve these savings without compromising quality remains an open research problem. As a case study, we are investigating search of spontaneous speech interviews with Holocaust eye-witnesses collected by the Shoah Foundation.
For example interviews, see: https://sfi.usc.edu/clipviewer
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