Hypothesis-driven adaptation (Hydra): a flexible eigenvoice architecture

In this article, a new architecture for speech recognition is introduced. As with many existing speech systems, this new approach involves multi-pass processing. In the present case, however, second-pass models are constructed on-line for each active hypothesis. Models for each hypothesized segment of the current utterance are constructed from linear combinations of "data cluster models" that have been trained on low-variability clusters of the training corpus. The data cluster weights are determined using an "eigenvoice" mechanism that is operative on low-complexity, low definition models. Once determined, the same weights are used to construct high-complexity, high-definition second-pass models generated over the same data clusters. Results from a simple recognition task are reported to demonstrate the interesting properties of the new architecture. The limitations, trade-offs and some possible extensions of the proposed approach are discussed