|
Research
in automatic Speaker Identification (SID) for non-commercial applications has
focused on telephone issues for several years. Researchers have focused on
problems such as recognizing a speaker on a cell-phone using a landline
telephone-based model. While this focus has improved performance, it has
ignored key problems such as far-field speaker recognition and SID in the
presence of noise. When speech data is
collected by microphones more than a few inches away from the speaker, room
acoustics and noise sources become important. In a room with lively
acoustics, echo and multi-path propagation become key concerns.
Conventional SID techniques often fail
because of mismatches between the data used for training and identification.
In this scenario, the identification data always comes from a live
microphone, but the training data might be from a landline telephone, a cell
phone, or another live microphone recording. A more serious mismatch may
occur if the system is trained on telephone data while the identification
data are recorded live at a higher sampling rate. This project aims to study
the far-field effects on current state-of-the-art SID systems and investigate
strategies to improve SID system performance in far-field scenarios.
|