Minimum detection error training of subword detectors

This paper presents methods and results for optimizing subword detectors in continuous speech. Speech detectors are useful within areas like detection-based ASR, pronunciation training, phonetic analysis, word spotting, etc. We propose a new discriminative training criterion for subword unit detectors that is based on the Minimum Phone Error framework. The criterion can optimize the F-score or any other detection performance metric. The method is applied to the optimization of HMMs and MFCC filterbanks in phone detectors. The resulting filterbanks differ from each other and reflect acoustic properties of the corresponding detection classes. For the experiments in TIMIT, the best optimized detectors had a relative accuracy improvement of 31.3% over baseline and 18.2% over our previous MCE-based method.