Auto Speech Tech Project2

Baseline

1. mkdir features in the baseline dir
2. change the data dir pathToDatabase to yourself in baseline_cqcc.m and baseline_mfcc.m
3. run baseline_cqcc.m like /matlab_dir/matlab -nodisplay -nodesktop -nosplash -r baseline_cqcc, then do same with the baseline_mfcc.m
4. u will get cqcc and mfcc features in dir features

NNET

in the nnet dir, we use some deep learning algorithm to solve this problem

Result

the column 3 and 4 only use train data the column 5 use train and dev data to test the eval

BaseLine

system	feature	EER(Dev)	EER(Eval)	EER(Eval)	Frequency Range	B	Remarks
GMM	cqcc	10.35	30.60	24.77	16-8000	96	Baseline !!!

GMM Approach

only one feature use in the gmm

system	feature	EER(Dev)	EER(Eval)	EER(Eval)	Frequency Range	B	iter(default 100)
GMM	mfcc	14.14	33.08		16-8000	256
GMM	mfcc	36.03	36.17		16-2000	256
GMM	mfcc	38.60	37.32		2000-4000	256
GMM	mfcc	6.86	27.60		4000-8000	256
GMM	mfcc	3.53	25.55		6000-8000	256
GMM	cqcc	13.44	28.50		16-8000	256
GMM	cqcc	40.45	37.98		16-2000	256
GMM	cqcc	42.04	39.59		2000-4000	256
GMM	cqcc	7.61	27.49		4000-8000	256
GMM	cqcc	4.82	20.30		6000-8000	256
GMM	cqcc	7.15	19.99		7000-8000	256
GMM	cqcc	4.99	18.05		6000-8000	512
GMM	cqcc	6.63	18.58		6000-8000	1024
GMM	cqcc	5.06	18.32		6000-8000	512	200
GMM	cqcc	6.64	18.58		6000-8000	1024	200
GMM	cqcc	7.56	18.07		7000-8000	512
GMM	cqcc	7.97	17.24	17.64	7000-8000	1024
GMM	cqcc	8.11	17.35	17.48	7000-8000	1024	200
GMM	cqcc	8.11	17.35	17.48	7000-8000	1024	300

combine all features in the gmm

NNET

system	feature	EER(Dev)	EER(Eval)	EER(Eval)	Frequency Range	B	Remarks
LCNN	cqcc	11.827	23.919	20.926
LCNN	fft	10.298	23.418	17.983
LCNN	fft	9.921	22.235	19.365