语种识别论文：语种识别 少数民族语 语音检测.doc

语种识别论文：语种识别 少数民族语 语音检测【中文摘要】据有关资料统计,当今世界上共有5651种语言。语种之间的信息互通越来越重要,如何让计算机识别出不同的语种已经成为人们的迫切需求。语种识别(Language Identification, LID),是计算机通过分析处理一段语音片断以判别其属于何种语言的过程,本质上也是属于语音识别的一个分支。它已经广泛的应用于信息检索和军事安全等领域。当前语种识别系统主要有三个方面：音位结构模型系统、声学模型系统以及联合系统。其中声学模型系统由于不需要人工标注语料,而且系统扩展性好,所以得到了广泛的应用。本文研究与文本无关的少数民族语种识别方法,采用高斯混合模型通用背景模型(Gaussian Mixture Model-Universal Background Mode, GMM-UBM)建立语种识别系统,探索提高识别率的途径。论文的主要工作有：(1)少数民族电话语音数据库的录制,该数据库包含了9个少数民族语和汉语普通话共10个语种,每个语种有50个发音人,男女比例约为1:1,并进行语音数据的整理。(2)构建一个基于GMM-UBM的少数民族语种识别系统,设计并实现语种识别实验：用双门限的方法进行语音检测；提取MFCC和SDC特征参数；训练UBM模型和GMM模型。(3)用6个民族的不同时长的测试语音和汉语借词进行测试。按四种测试语料分别给出各个语种的识别率,对比两种特征的识别率,从语音学的角度分析各语种之间误识别的原因,还分析了汉语借词对少数民族语种识别率的影响。实验结果表明,论文设计的基于GMM-UBM的语种识别系统具有较好的扩展性；双门限的方法进行语音检测,能有效地去除噪音,提取到有用的语音帧；基于SDC特征参数的少数民族语种识别率明显高于基于MFCC特征参数的识别率；6个语种之间存在有规律的误识别；对汉语借词的识别,实验结果表明汉语借词会使少数民族语种识别率明显下降。【英文摘要】According to statistics, there are 5651 languages in the world. With the communication between the languages becomes more and more important, how to make computer identify the different languages have become the peoples urgent needs. Language identification is the process of determining the language of a spoken utterance. In essence, it is an aspect of speech recognition. Language identification has been widely used in multi-linguistic information services and security fields.There are three types of language recognition system:phonotactic approach system in current, acoustic approach system and their combined systems. Acoustic approach system does not require manual tagging corpus, and it has a good portable, so it has been widely used.This paper is focus on the test-independent language identification method, using GMM-UBM to build the language identification system, and explores methods to improve the recognition rate. The main works are as follows:(1) We design a minority-oriented language identification of telephone speech corpus, which consists of spontaneous utterances in 9 minority-oriented languages and Mandarin. The utterances are produced by 25 male and 25 female, in each language over real telephone lines. Then we do some preliminary collating before we use them.(2) A language identification system of minority language based on GMM-UBM model is built in this paper. And we design two language identification experiments, in which MFCC and SDC feature parameters are respectively used. In the experiments, a new method of double threshold for voice activity detection is used to effectively remove noise and extract useful voice frames. Then we extract MFCC and SDC feature parameters, and train UBM model and the GMM model of 6 languages.(3) Utterances with different durations and Chinese loan words of six minority languages are selected to test. We analyze each language identification rate and the results with different duration testing data and different feature parameters, and then we give some explanations of error identification in terms of phonetics. We also analyze the impact of Chinese loan words on the results.Experimental results show that our proposed language identification system based on GMM-UBM model have better expansibility and applicability; The method of double threshold for voice activity detection can effectively remove noise and extract useful voice frames; Experiments used SDC feature parameters have better performance than those used MFCC feature parameters; the performance of the minority language identification would significantly decline when Chinese loan words exist.【关键词】语种识别 少数民族语 语音检测 GMM-UBM 汉语借词【英文关键词】Language Identification Minority Language Voice Activity Detection GMM-UBM Chinese Loan Words【目录】基于电话语音的少数民族语种识别研究摘要3-4Abstract4-5第一章 绪论8-161.1 引言8-91.2 语种识别概述9-131.2.1 语种识别的原理9-101.2.2 语种识别研究的历史10-111.2.3 语种识别的主要方法11-121.2.4 语种识别系统评估与数据库12-131.3 论文的研究思路和作者的工作13-151.3.1 研究思路13-141.3.2 作者的工作14-151.4 论文的组织15-16第二章 电话语音库的录制16-232.1 引言16-172.2 语音数据库的设计17-192.2.1 发音人17-182.2.2 录音内容18-192.3 录音软件的开发19-202.4 录音和数据整理20-222.5 本章小结22-23第三章 基于GMM-UBM的语种识别23-373.1 引言23-243.2 语音信号预处理24-253.3 MFCC特征参数25-273.4 SDC特征参数27-283.5 高斯混合模型28-323.5.1 GMM模型的基本概念28-303.5.2 GMM模型的训练30-313.5.3 基于GMM模型的语种识别31-323.6 GMM-UBM模型