汉语语音识别的平滑声韵基元HMM算法

摘要

The base unit in mandarin speech recognition is phoneme, semi-syllable or syllable. Semi-syllable system has fewer HMM models and needs less computation, thus it's suitable for real-time systems. But due to poor description for the acoustic properties of the speech signal, it generally shows a low performance compared with syllable system. While the system based on syllable or phoneme (tri-phone or di-phone) has much more HMM models, and needs massive computation in training and recognition, which goes against to real-time implementation. The new scheme is a compromised one. The new system is based on semi-syllable system, but the parameters of the entire syllable are used in training phase, so smoothing between two semi-syllable units is introduced. The transition probability between semi-syllables is calculated, and the two semi-syllable HMMs are connected into a full syllable HMM in recognition phase. This can increase the system performance without increasing HMM models, and it's fit for real-time systems with DSP kernel.%汉语语音识别的基本单元一般为音素、音节以及声韵母.以声韵母为基元的识别系统由于HMM模型较少，计算量小，适合于实时实现.但是由于模型比较孤立，对语音信号的声学特性描述得不够精确，因而识别率一般比音节基元的系统低.而以音节、音素（tri-phone、di-phone）为基元的系统则有HMM模型数量多、训练和识别过程中计算量大的缺点，影响到系统的实时性.本文提出了一种折衷的方案，系统基元仍选择声韵母，而在HMM训练阶段，对整个音节序列的参数进行运算，使声韵过渡段的状态得到平滑，同时计算并保存每个音节声韵之间的转移概率，识别时动态组装为完整的音节HMM.该方法在保持少量HMM个数的同时，能够降低误试率，适合于以DSP为核心的实时连接词语音识别系统.