Speaker Identification Investigation and Analysis in Unbiased and Biased Emotional Talking Environments
This work addresses speaker identification for audio processing in emotional contexts, but it is incremental as it applies an existing method to new emotional data.
The paper tackled speaker identification in unbiased and biased emotional talking environments using Suprasegmental Hidden Markov Models, finding that performance was superior in biased environments and results were close to human subjective assessments.
This work aims at investigating and analyzing speaker identification in each unbiased and biased emotional talking environments based on a classifier called Suprasegmental Hidden Markov Models (SPHMMs). The first talking environment is unbiased towards any emotion, while the second talking environment is biased towards different emotions. Each of these talking environments is made up of six distinct emotions. These emotions are neutral, angry, sad, happy, disgust and fear. The investigation and analysis of this work show that speaker identification performance in the biased talking environment is superior to that in the unbiased talking environment. The obtained results in this work are close to those achieved in subjective assessment by human judges.