4 accepted papers
Contextual biasing enables speech recognizers to transcribe important phrases in the speaker’s context, such as contact names, even if they are rare in, or absent from, the training data. Attention-based biasing is a leading approach which allows for full end-to-end cotraining of the recognizer and…
In this work, we present a model-based approach to improving contextual biasing that improves quality without drastically increasing model computation during inference. Specifically, we look at injecting text data during training which is representative of contextually-relevant context that will be…
Automatic speech recognition (ASR) systems often have difficulty recognizing long-tail entities such as contact names and local restaurant names, which usually do not occur, or occur infrequently, in the system’s training data. In this work, we present a method which uses learned text embeddings and…
In this paper, we present a pruning technique for maximum entropy (MaxEnt) language models. It is based on computing the exact entropy loss when removing each feature from the model, and it explicitly supports backoff features by replacing each removed feature with its backoff. The algorithm compute…