Vaibhav

Thanks for helping out Is there any other thing i can try to reduce WER % ?

I saw wrong file Sorry . It was 40 How can i improve accuracy ?

testing speakers are different phone set size = 170 Yes , testing words exist in the the 1400 words What can be done ? Also i had tried training and testing on same speakers but again the WER was in range 60-75 %

testing speakers are different phone set size = 170 Yes , testing words exist in the the 1400 words

28 speakers for training and 4 for testing 90 minutes training data . vocab size is 1400 words training . I had trained using mono , tri1 , tri2 , tri3 and sgmm models but all are giving wer in range 55-65

i am not using any standard database . I am having my own dataset of Punjabi language which is tonal language . So i thought it would be good to add pitch features with mfcc but the results are not good with or without pitch features . What can i do ?

I am facing one more issue sir I had run mfcc + pitch script with multiple available options --add-pov-feature , --add-normalized-pitch etc . but i am getting a WER % of about 55-60 % Please suggest what can i do ? Thanks

Ok Thanks for your help sir