Using Second-order Hidden Markov Model To Improve Speaker Identification Recognition Performance Under Neutral Condition
2017 Β· Ismail Shahin
Abstract
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%.
Authors
(none)
Tags
Stats
Related papers
- Speaker Identification In The Shouted Environment Using Suprasegmental Hidden Markov Models (2017)10.85
- Emirati Speaker Verification Based On Hmm1s, Hmm2s, And Hmm3s (2017)5.24
- Identifying Speakers Using Their Emotion Cues (2018)10.85
- Gender-dependent Emotion Recognition Based On Hmms And Sphmms (2018)9.59
- Speaker Recognition With Random Digit Strings Using Uncertainty Normalized Hmm-based I-vectors (2019)8.82
- Novel Cascaded Gaussian Mixture Model-deep Neural Network Classifier For Speaker Identification In Emotional Talking Environments (2018)12.74
- A Speech Enhancement Algorithm Based On Non-negative Hidden Markov Model And Kullback-leibler Divergence (2020)5.84
- Multi-label Training For Text-independent Speaker Identification (2022)0.00