说话人识别python_基于各种分类算法的说话人识别（年龄段识别）

基于各种分类算法的语音分类(年龄段识别)

概述

实习期间作为帮手打杂进行了一段时间的语音识别研究，内容是基于各种分类算法的语音的年龄段识别，总结一下大致框架，基本思想是：

获取语料库

TIMIT

提取数据特征，进行处理

MFCC/i-vector

LDA/PLDA/PCA

语料提取，基于分类算法进行分类

SVM/SVR/GMM/GBDT...

用到的工具有HTK(C,shell)/Kaldi(C++,shell)/LIBSVM(Python)/scikit-learn(Python)

获取语料库

PS：

TIMIT的语料语音(即子文件夹下的WAV文件)是SPHERE文件，可以用Kaldi转换

TIMIT/DOC/SPKRINFO.TXT中为speaker信息，作为分类条件

提取数据特征，进行处理

将SPHERE文件转换为WAV文件

Kaldi中tools下有SPHERE文件转换工具sph2pipe.exe

cd kaldi/kaldi-trunk/tools/sph2pipe_v2.5/

转换方法

sph2pipe -f wav sourcefile targetfile

用re_sph2pipe.py脚本生成sph2pipe转换文件

#encoding="utf-8"

import os

import os.path

rootdir = "E:/vc/TIMIT"

timitpath = "/home/zhangzd/kaldi/kaldi-trunk/TIMIT"

sph2pipepath = "/home/zhangzd/kaldi/kaldi-trunk/tools/sph2pipe_v2.5/sph2pipe"

f = open('E:/vc/data/mfcc/make_sph2pipe_file.txt','w')

for root,dirs,files in os.walk(rootdir):

for fn in files:

if fn[len(fn)-3:len(fn)]=='WAV':

sourcefile = timitpath+root[len(rootdir):]+"/"+fn

targetfile = root[len(root)-5:len(root)]+"_"+fn

s = sph2pipepath + " -f wav " + sourcefile+" "+targetfile+"n"

f.write(s.replace('\','/'))

f.close()

得到的转换文件make_sph2pipe_file.txt如下

/home/zhangzd/kaldi/kaldi-trunk/tools/sph2pipe_v2.5/sph2pipe -f wav /home/zhangzd/kaldi/kaldi-trunk/TIMIT/CONVERT/SA1.WAV NVERT_SA1.WAV

/home/zhangzd/kaldi/kaldi-trunk/tools/sph2pipe_v2.5/sph2pipe -f wav /home/zhangzd/kaldi/kaldi-trunk/TIMIT/TIMIT/TEST/DR1/FAKS0/SA1.WAV FAKS0_SA1.WAV

/home/zhangzd/kaldi/kaldi-trunk/tools/sph2pipe_v2.5/sph2pipe -f wav /home/zhangzd/kaldi/kaldi-trunk/TIMIT/TIMIT/TEST/DR1/FAKS0/SA2.WAV FAKS0_SA2.WAV

...

最后在linux下执行shell命令

#!bin/sh

while read line

do

echo $line

done make_sph2pipe_file.txt

PS：

f.write(s.replace('\','/'))是因为在windows下用\表示路径，在linux下用/表示

在Kaldi中生成MFCC特征

解析/home/zhangzd/kaldi/kaldi-trunk/egs/wsj/s5/steps/make_mfcc.sh

中提取特征代码为

$cmd JOB=1:$nj $logdir/make_mfcc_${name}.JOB.log

compute-mfcc-feats --verbose=2 --config=$mfcc_config

scp,p:$logdir/wav_${name}.JOB.scp ark:- |

copy-feats --compress=$compress ark:-

ark,scp:$mfccdir/raw_mfcc_$name.JOB.ark,$mfccdir/raw_mfcc_$name.JOB.scp

|| exit 1;

即生成MFCC命令为

compute-mfcc-feats --verbose=2 --config=config.txt scp,p:scp.txt ark:-|copy-feats ark:- ark,scp:mfcc.ark,mfcc.scp

config.txt格式为

--use-energy=false # only non-default option.

...

scp.txt格式为

FAKS0_SA1 /home/zhangzd/kaldi/kaldi-trunk/src/test/FAKS0_SA1.WAV

mfcc.scp格式为

FAKS0_SA1 /home/zhangzd/kaldi/kaldi-trunk/src/test/mfcc.ark

mfcc.ark会自动生成

HTK中生成MFCC特征

HTK更为简单

HCopy -c config.txt -S scp.txt

config.txt格式为

SOURCEFORMAT = WAV # Gives the format of the speech files

TARGETKIND = MFCC_0_D_A # Identifier of the coefficients to use

# Unit = 0.1 micro-second :

WINDOWSIZE = 250000.0 # = 25 ms = length of a time frame

TARGETRATE = 100000.0 # = 10 ms = frame periodicity

NUMCEPS = 12 # Number of MFCC coeffs (here from c1 to c12)

USEHAMMING = T # Use of Hamming function for windowing frames

PREEMCOEF = 0.97 # Pre-emphasis coefficient

NUMCHANS = 26 # Number of filterbank channels

CEPLIFTER = 22 # Length of cepstral liftering

ENORMALIZE = T

scp.txt格式为

E:vcdatatimitFADG0_SA1.WAV E:vcdatamfccFADG0_SA1.mfcc

E:vcdatatimitFADG0_SA2.WAV E:vcdatamfccFADG0_SA2.mfcc

E:vcdatatimitFADG0_SI1279.WAV E:vcdatamfccFADG0_SI1279.mfcc

...

其他

i-vector

vad