Speaker Identification in a Shouted Talking Environment Based on Novel Third-Order Circular Suprasegmental Hidden Markov Models

It is well known that speaker identification yields very high performance in a neutral talking environment; on the other hand, the performance has been sharply declined in a shouted talking environment. This work aims at proposing, implementing, and evaluating novel third-order circular suprasegmental hidden Markov models (CSPHMM3s) to improve the low performance of text-independent speaker identification in a shouted talking environment. CSPHMM3s possess combined characteristics of: Circular Hidden Markov models, third-order hidden Markov models (HMM3s), and suprasegmental hidden Markov models (SPHMMs). Our results show that CSPHMM3s are superior to each of: first-order left-to-right suprasegmental hidden Markov models (LTRSPHMM1s), second-order left-to-right suprasegmental hidden Markov models (LTRSPHMM2s), third-order left-to-right suprasegmental hidden Markov models (LTRSPHMM3s), first-order circular suprasegmental hidden Markov models (CSPHMM1s), and second-order circular suprasegmental hidden Markov models (CSPHMM2s) in a shouted talking environment. Using our collected speech database, average speaker identification performance in a shouted talking environment based on LTRSPHMM1s, LTRSPHMM2s, LTRSPHMM3s, CSPHMM1s, CSPHMM2s, and CSPHMM3s is 74.6, 78.4, 81.7, 78.7, 83.4, and 85.8 %, respectively. Speaker identification performance that has been achieved based on CSPHMM3s is close to that attained based on subjective assessment by human listeners.