基于仿生模式识别理论的声调识别

基音频率轨迹能比较真实地反映汉语普通话中的声调特性，通过识别不同的基音轨迹来识别声调，是一种较好的方法。根据仿生模式识别理论，提出用迭代自组织数据分析算法(ISODATA)寻找覆盖区中心，运用多权值神经网络对每个聚类中心实现覆盖的方法，实现四种声调的识别。通过实验与隐马尔科夫模型(HMM)和支持向量机(SVM)算法比较，在少量样本的情况下，能得到相对较高的识别率。

Tone recognition based on biomimetic pattern recognition theory

Pitch frequency trajectories can more truly reflect the tone characteristics of Mandarin. It is a better way to identify tone by identifying different pitch trajectories. According to the theory of biomimetic pattern recognition, an improved tone recognition algorithm was proposed. It used Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to find centers of coverage areas, and multi-weight neural network to achieve coverage for each cluster center. The experimental results show that in the case of a small sample, the proposed algorithm can get a higher recognition rate than that of Hidden Markov model (HMM) and Support Vector Machine (SVM).