Speaker identification using Hidden Conditional Random Field-based speaker models
Wei‐Tyng Hong · 2010
In this paper we make a study of applying Hidden Conditional Random Fields (HCRF) to establish speaker models. A novel training algorithm combining the discriminative training criterion with HCRF for speaker identification is proposed. This work also adopted discriminative training technique to train GMM, HMM, and HCRF speaker models respectively; and the performance of speaker identification by the three speaker models with different amounts of training speech for clean and noisy testing speech were investigated. The experimental results indicate that the HCRF model consistently achieved the lowest error rate among the three models regardless of the length of the test and training speech and presence of noise.