基于HALTH、CHMM2s和SPHALTH的紧张情绪环境下语音识别研究与增强
Studying and Enhancing Talking Condition Recognition in Stressful and Emotional Talking Environments Based on HMMs, CHMM2s and SPHMMs
摘要：
本研究的工作是致力于研究和提高在紧张和情绪化的谈话环境（完全两个独立的环境）的基础上，三个不同的和独立的分类器的谈话条件识别。这三个分类器是：隐马尔可夫模型（HHMM），二阶循环隐马尔可夫模型（CHMM 2）和超分段隐马尔可夫模型（SPHMM）。本研究中所使用的压力性谈话环境由中性、喊叫、缓慢、大声、柔和和快速的谈话条件组成，而情绪性谈话环境由中性、愤怒、悲伤、快乐、厌恶和恐惧情绪组成。在目前的工作中所取得的成果表明，SPHM导致每一个HHM和CHMM2在提高说话条件识别在紧张和情绪化的谈话环境。实验结果还表明，在紧张的谈话环境中的谈话条件识别比在情绪化的谈话环境中的分别提高了2.7%，1.8%和3.3%，基于Hacks，CHMM 2s和SPHacks。根据人类法官的主观评估，压力谈话条件下的识别性能领先于情绪条件下的识别性能5.2%。
英文摘要：
The work of this research is devoted to studying and enhancing talking condition recognition in stressful and emotional talking environments (completely two separate environments) based on three different and separate classifiers. The three classifiers are: Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s) and Suprasegmental Hidden Markov Models (SPHMMs). The stressful talking environments that have been used in this work are composed of neutral, shouted, slow, loud, soft and fast talking conditions, while the emotional talking environments are made up of neutral, angry, sad, happy, disgust and fear emotions. The achieved results in the current work show that SPHMMs lead each of HMMs and CHMM2s in improving talking condition recognition in stressful and emotional talking environments. The results also demonstrate that talking condition recognition in stressful talking environments outperforms that in emotional talking environments by 2.7%, 1.8% and 3.3% based on HMMs, CHMM2s and SPHMMs, respectively. Based on subjective assessment by human judges, the recognition performance of stressful talking conditions leads that of emotional ones by 5.2%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
