Different approaches for speaker identification and verification have been studied in our Institute.
Text independent speaker identification based on Vector Quantization. This method provided good results for quite small sets of speakers (less than 100). The method has also been succefully used to combine acoustic features with visual ones (derived from the analysis of the face).
Text independent speaker identification based on Neural Network. This approach integrates Competitive Neural Networks with Radial Basis Function Networks to perform the task.
Text independent speaker identification and verification based on Continuous Density Hidden Markov Models. The method provides better results, for identification, than the previous ones.
Text dependent speaker identification and verification based on Semi Continuous Hidden Markov Models. The method is very promising for speaker verification purposes because allows to verify (accept or reject) both the speaker identity and the content of the input utterance. The method requires to define and record a selected set of training utterances, for each reference speaker, in order to design a corresponding set of phoneme models. Experiments led on theAPASCI database (138 speakers) provided an identification error equal to 1.3 % and an equal error rate of 1.0 %.
Finally, a prototype system that integrates both acoustic and visual features has been developed. This integration method could be efficiently used for combining other types of biometric features, such as fingerprints or iris scanning.
This page is maintained by Daniele Falavigna.