12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Optimized Feature Extraction and HMMs in Subword Detectors

Alfonso M. Canterla, Magne H. Johnsen

NTNU, Norway

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 build detectors for both articulatory features and phones by discriminative training of detector-specific MFCC filterbanks and HMMs. The resulting filterbanks are clearly different from each other and reflect acoustic properties of the corresponding detection classes. For the TIMIT task, our detector-specific features reduce the average detection error rate by 20% compared to standard MFCCs.

Full Paper

Bibliographic reference.  Canterla, Alfonso M. / Johnsen, Magne H. (2011): "Optimized feature extraction and HMMs in subword detectors", In INTERSPEECH-2011, 2397-2400.