Speech Recognition Using Broad Classes

This work explores the use of speech knowledge for robust speech recognition by first describing the speech signal through a set of broad speech units, and then conducting a more detailed analysis from these broad units. These units are formed by grouping together parts of the acoustic signal that have similar temporal and spectral characteristics. This work first introduces a novel instantaneous adaptation technique to robustly detect broad classes (BCs) from the input signal using the Extended Baum-Welch (EBW) transformations. Recognition experiments indicate that the EBW method offers a 5% relative improvement compared to typical adaptation approaches. Next, we explore utilizing BC knowledge as a pre-processor for segment-based speech recognition systems. Recognition experiments indicate that utilizing BC knowledge as a pre- processor offers a 14% relative improvement over the baseline recognizer in noisy conditions. Finally, this thesis investigates using BC knowledge for island-driven search. Experiments indicate that the island-driven search strategy results in a 3% improvement in accuracy and also provides faster computation time.

Autorentext

Tara Sainath received her PhD from MIT in 2009 and then joined the speech recognition group at IBM. She has organized a special session on sparse representations at Interspeech 2010 and has served as a staff reporter for the IEEE Speech and Language Processing Technical Committee Newsletter. Her main research interests are in acoustic modeling.