Home Training a Speech Recogniser with HCS vLab

Training a Speech Recogniser with HCS vLab

I just received a report from Matt Atcheson, one of our HDR testers at UWA, with the results of some work he’s done on evaluating the HTK integration with the HCS vLab. Matt used my template Python interface to download audio files from the vLab and feed them to the HTK training algorithms to train a digit string recogniser. He was then able to test the recogniser on unknown data also downloaded from the vLab.

The results were interesting:

Using the full set of digit recordings that I could find (about 940 of them), setting aside 10% for testing, and with a grammar that constrains transcripts to exactly four digits, I get about 99% word accuracy, and about 95% sentence accuracy.

====================== HTK Results Analysis =======================

Date: Tue Jan 28 21:08:50 2014

Ref : >ntu/hcsvlab_api_testing_matt/digitrec/data/testing_files/testref1.mlf

Rec : >buntu/hcsvlab_api_testing_matt/digitrec/data/testing_files/recout.mlf

———————— Overall Results ————————–

SENT: %Correct=94.74 [H=90, S=5, N=95]

WORD: %Corr=98.95, Acc=98.42 [H=564, D=3, S=3, I=3, N=570]

Matt also gave us some good feedback based on his experiments. If there are other testers interested in trying to repeat this experiment or explore a bit on their own, Matt’s code is available on BitBucket.

To run his experiments, Matt made use of a virtual machine on the Nectar Research Cloud. Any Australian researcher can login to the cloud and get a free allocation of virtual machines. We’ve made a VM image (called ‘HCSvLab Tools’, listed in the Public list of snapshots on your dashboard) that has HTK, DeMoLib and INDRI pre-installed; as a user, you can create your own instance of this image and start working with these tools.

This post is licensed under CC BY 4.0 by the author.