This paper describes using speaker ID techniques to identify repeat callers in a spoken dialog system, using only acoustic features. Often it is useful to know if a dialog user is a novice or is experienced, and it can be the case that identifying data such as Caller ID is either unreliable or unavailable. Our approach attempts to remedy this by determining user identity in a dialog session using the acoustic information in the dialog. We optimize the audio content of each call by removing artifacts not relevant to modeling speech. This technique is applied to finding consecutive callers and creating unique user identities over all calls over a larger time frame, with the aim of tuning or adapting the dialog system based on the user identity. Our results show that the technique is effective in recognizing consecutive callers and in identifying a unique user identities in a large set of calls.
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