Speaker Identification in Shouted Talking Environments Based on Novel Third-Order Hidden Markov Models
This addresses the problem of low speaker identification accuracy in noisy environments for applications like security or communication systems, but it is incremental as it builds on existing HMM methods.
The paper tackled speaker identification in shouted talking environments by proposing Third-Order Hidden Markov Models (HMM3s), which improved performance by 11.3% over second-order and 166.7% over first-order models.
In this work we propose, implement, and evaluate novel models called Third-Order Hidden Markov Models (HMM3s) to enhance low performance of text-independent speaker identification in shouted talking environments. The proposed models have been tested on our collected speech database using Mel-Frequency Cepstral Coefficients (MFCCs). Our results demonstrate that HMM3s significantly improve speaker identification performance in such talking environments by 11.3% and 166.7% compared to second-order hidden Markov models (HMM2s) and first-order hidden Markov models (HMM1s), respectively. The achieved results based on the proposed models are close to those obtained in subjective assessment by human listeners.