Using Second-Order Hidden Markov Model to Improve Speaker Identification Recognition Performance under Neutral Condition
This is an incremental improvement for speaker identification systems in text-dependent scenarios under neutral conditions.
The paper tackled the problem of improving speaker identification performance under neutral talking conditions by using a second-order hidden Markov model (HMM2), resulting in a 9% improvement in recognition performance compared to a first-order HMM.
In this paper, second-order hidden Markov model (HMM2) has been used and implemented to improve the recognition performance of text-dependent speaker identification systems under neutral talking condition. Our results show that HMM2 improves the recognition performance under neutral talking condition compared to the first-order hidden Markov model (HMM1). The recognition performance has been improved by 9%.