数据采集与处理2019，Vol.34Issue(2)：322-330,9.DOI:10.16337/j.1004-9037.2019.02.015
基于卷积神经网络的语种识别系统
Language Identification Based on Convolutional Neural Network
摘要
Abstract
A key problem of language identification (LID) is how to design effective representations which are specific to language information. Recent advances in deep neural networks (DNNs) have led to significant improvements in language identification. The acoustic feature extracted from a structured DNN which is discriminative to phoneme or tri-phone states can significantly improve the performance. End-toend schemes also show its strong capability of modelling in recent years. A novel end-to-end convolutional neural network (CNN) LID system is proposed, called language identification network (LID-net), taking advantage of neural networks (NNs) with the capability in feature extraction and discriminative modelling, which can extract units that discriminant to languages, and we call them LID-senones, thus can extract effective utterance representation with pooling layer. Evaluations on NIST LRE 2009 show improved performance compared to current state-of-the-art deep bottleneck feature with total variability (DBF-TV) method, can achieve 1.35％, 12.79％ and 29.84％ relative equal error rate (EER) improvement on 30, 10 and 3 s utterances and receive over 30％ relative gain in Cavgon all durations.关键词
语种识别/卷积神经网络/语音段表示/语种区分性基本单元/端对端机制Key words
language identification/convolutional neural network/utterance representation/language identification (LID)-senone/end-to-end scheme分类
信息技术与安全科学引用本文复制引用
金马,宋彦,戴礼荣..基于卷积神经网络的语种识别系统[J].数据采集与处理,2019,34(2):322-330,9.基金项目
国家自然科学基金(U1613211)资助项目 （U1613211）
