DOMAIN AND SPEAKER ADAPTATION FOR CORTANA SPEECH RECOGNITION

Voice assistant represents one of the most popular and important scenarios

for speech recognition. In this paper, we propose two adaptation

approaches to customize a multi-style well-trained acoustic

model towards its subsidiary domain of Cortana assistant. First, we

present anchor-based speaker adaptation by extracting the speaker

information, i-vector or d-vector embeddings, from the anchor segments

of ‘Hey Cortana’. The anchor embeddings are mapped to

layer-wise parameters to control the transformations of both weight

matrices and biases of multiple layers. Second, we directly update

the existing model parameters for domain adaptation. We demonstrate

that prior distribution should be updated along with the network

adaptation to compensate the label bias from the development

data. Updating the priors may have a significant impact when the target

domain features high occurrence of anchor words. Experiments

on Hey Cortana desktop test set show that both approaches improve

the recognition accuracy significantly. The anchor-based adaptation

using the anchor d-vector and the prior interpolation achieves 32%

relative reduction in WER over the generic model.