Comparative experiments on large vocabulary speech recognition

We describe recent changes to the BYBLOS system´s training and recognition algorithms and report on numerous experiments in large vocabulary speech recognition. In earlier work, we performed five key experiments that were designed to answer questions related to different training scenarios. We investigated (1) the effect of varying the number of training speakers if the total amount of training data remains constant, (2) data pooling versus model averaging for generating speaker-independent (SI) HMMs, (3) the benefit of doubling the acoustic training data, (4) SI versus SD performance when the SI training data is twelve times greater, (5) the effect of cross-domain training for both the acoustic and language models. Our recent work was focused on four specific problem areas sharing the common thread that the test condition exposes the recognizer to phenomena not observed in the training data. Here we investigated (1) words outside the vocabulary, (2) spoken language effects due to subject variability and spontaneous dictation, (3) non-native dialects of the language, and (4) new microphones not used in training