Speaker Identification in each of the Neutral and Shouted Talking Environments based on Gender-Dependent Approach Using SPHMMs
This work addresses speaker identification in noisy environments, but it is incremental as it builds on existing models with a gender-dependent tweak.
The paper tackled the problem of speaker identification performance declining sharply in shouted talking environments by proposing a gender-dependent approach using Suprasegmental Hidden Markov Models (SPHMMs), resulting in improvements of about 6% and 8% over baseline methods.
It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identification performance is declined sharply in the shouted talking environments. 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.