摘要
本文首先讨论了汉语方言辨识的依据及特征选取的基本原则,并由此导出了区间差分倒谱特征。然后利用GMM符号发生器和N元语言模型及ANN建立了一个方言辨识系统,该系统与传统的语种识别系统相比,具有以下特点:第一,系统不需要标注好的语音库,从而降低了汉语方言语音库建设的劳动强度和要求;第二,GMM符号化器计算量远远低于音素辨识器,从而提高了方言辨识速度,便于今后实时处理。第三,具有更高的辨识效果和更好的容错性。汉语普通话和三种方言辨识实验结果表明,系统平均辨识率可以达到83.8%。
This paper discusses the criterions for distinguishing different Chinese dialects and the basic features selection firstly. According to these principals, a novel feature named district differential cepstral feature was proposed. Then, a novel dialect identification system combining GMM tokenizer, N-gram language model and ANN is constructed. Compared with traditional LID system, the new system has following characteristics： first, it is unnecessary to use tagged dialects speech database ,which becomes less labour-intensive to build corpora. Second, GMM tokenizer is more computationally efficient. Third, the system has more accurate and robust performance. In a test under Chinese dialects classification, averagely 83.8% accuracy is achieved.
出处
《中文信息学报》
CSCD
北大核心
2006年第5期77-82,共6页
Journal of Chinese Information Processing
基金
江苏省"十五"社科基金资助项目(K3-013)
江苏省高校自然科学基金资助项目(99KJB510002)
关键词
计算机应用
中文信息处理
GMM符号化器
N元语言模型
汉语方言辨识
computer application
Chinese information processing
GMM tokenizer
n-gram language modeling
Chinese dialects identification
