  Speaker verification is the process by which a speakers claim of identity is
tested against a claimed speaker by his or her voice. Speaker verification is
done by the use of some parameters (features) from the speakers voice which can
be used to differentiate among many speakers. The efficiency of speaker
verification system mainly depends on the feature set providing high
inter-speaker variability and low intra-speaker variability. There are many
methods used for speaker verification. Some systems use Mel Frequency Cepstral
Coefficients as features (MFCCs), while others use Hidden Markov Models (HMM)
based speaker recognition, Support Vector Machines (SVM), GMMs . In this paper
simple intra-pitch temporal information in conjunction with pitch synchronous
cepstral coefficients forms the feature set. The distinct feature of a speaker
is determined from the steady state part of five cardinal spoken English
vowels. The performance was found to be average when these features were used
independently. But very encouraging results were observed when both features
were combined to form a decision for speaker verification. For a database of
twenty speakers of 100 utterances per speaker, an accuracy of 91.04% has been
observed. The analysis of speakers whose recognition was incorrect is conducted
and discussed .
