A high performance text independent speaker recognition system based on vowel spotting and neural nets

We present a text independent speaker recognition system based on vowel spotting and feed forward multilayer perceptrons (MLPs). The perceptual linear predictive (PLP) speech analysis technique was used for parameter estimation, a feed forward MLP for vowel spotting and a simple MLP for the classification procedure. To train and test the system we used the TIMIT database. We conclude with results of the speaker verification and identification process, showing that the system described has a high recognition accuracy (~98%) using short test utterances (2.5 sec). It also has a real-time response, is easily adapted to new speakers and requires a small amount of data for training purposes (three sentences per speaker)