Very low complexity prompted speaker verification system based on HMM-modeling

The complexity of the algorithms used in speaker verification is often not suitable for low power applications or HW platforms having small portions of memory available (smartcard). This paper presents a text-prompted recognition system composed of two blocks performing speaker verification and text confirmation. In both cases we made an effort to use low complexity HMM models of voiceprint and phonemes to be able to adopt strong simplification in probability computation. As far as the normalization process is usually the computationally most intensive process, we propose new efficient normalization techniques for both speaker verification and text confirmation. Low complexity is also achieved adopting smart quantization of features and model parameters. In spite of simplicity we report an Equal Error Rate (EER) of 1.2% for speaker verification and of 0.7% for text confirmation using 3 second speech utterances. Memory requirements are also kept small: less than 450 bytes are needed for speaker voiceprints.