基于倒谱距离窗移最小失真分割的语种辨识

Language Identification Based on Minimum Distortion of Cepstrum Distance Segmentation

提出一种语种辨识的新方法.采用一种无需对语音文件进行标注的方法,提出基于倒谱距离窗移最小失真分割子词,在语种辨识前端用子词的自动分割方法把语音信号分割成许多子词.对得到的所有子词进行聚类并对每一类建立一个隐马尔可夫模型(HMM),最后利用得到的所有的子词模型对输入语音进行语种辨识.实验表明,该方法是一种简洁而且有效的语种辨识方法.

We propose a novel approach to language identification. Generally speaking, an ideal language identification system needs a large number of speech transcriptions at the phoneme level for training the phone model, involving a huge amount of work and cost. In this project, we use a rough segmentation instead of transcription to produce sub-words, and a front-end sub-words recognizer for individual languages to be identified. This is followed by clustering the sub-words and creating an HMM for each cluster. Preliminary results on language identification are provided to demonstrate simplicity and effectiveness of this approach.