Ismail Shahin
Gender recognition, hidden Markov models, Mel frequency cepstral coefficients, speaker recognition, suprasegmental hidden Markov models
It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identifica- tion performance is declined sharply in the shouted talking envi- ronments. This work aims at proposing, implementing and testing a new approach to enhance the declined performance in the shouted talking environments. The new proposed approach is based on gender-dependent speaker identification using suprasegmental hidden Markov models (SPHMMs) as classifiers. This proposed approach has been tested on two different and separate speech databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. The results of this work show that gender-dependent speaker identification based on SPHMMs outperforms gender-independent speaker identification based on the same models and gender-dependent speaker identification based on hidden Markov models (HMMs) by about 6 and 8%, respectively. The results obtained based on the proposed approach are close to those obtained in subjective evaluation by human judges.
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