基于隐条件随机场的声调建模及区分性模型权重训练

摘要

为提高声调识别率,利用隐条件随机场对汉语声调进行建模,通过加入音节内动态特征、音节间动态特征以及段长特征来进一步提高声调识别性能.提出了将声调模型加入大词汇量连续语音识别系统的区分性方法,根据最小音子错误准则区分性训练模型相关的概率权重,对声学模型及声调模型概率进行加权;给出了两种权重组合策略并通过一种平滑方法来克服权重训练过拟合的问题.实验结果表明,基于隐条件随机场声调模型能够显著提高声调识别率以及大词汇量语音识别的识别率,同时与全局模型权重方法比较,区分性的模型权重训练能够在声调模型加入连续语音识别系统之后,进一步提高系统的识别性能.%The use of hidden conditional random fields (HCRFs) for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training (DMWT) is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error (MPE) criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition (LVCSR) can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.