Abstract: Presented herein are techniques for augmenting a speech recognition engine. According to the disclosed techniques, audio data is obtained as part of an automatic speech recognition session. Speech hints are also obtained as part of the automatic speech recognition session. A dynamic language model is generated from the speech hints for use during the automatic speech recognition session. A combined language model is then generated from the dynamic language model and a static language model. Finally, the audio data is converted to text using the combined language model as part of the automatic speech recognition session.

Abstract: A method for implementing a privacy-preserving automatic speech recognition system using federated learning. The method includes receiving, from respective client devices, at a cloud server, local acoustic model weights for a neural network-based acoustic model of a local automatic speech recognition system running on the respective client devices, wherein the local acoustic model weights are generated at the respective client devices without labelled data, updating a global automatic speech recognition system based on (a) the local acoustic model weights received from the respective client devices and (b) global acoustic model weights of the global automatic speech recognition system derived from labelled data to obtain an updated global automatic speech recognition system, and sending the updated global automatic speech recognition system to the respective client devices to operate as a new local automatic speech recognition system.

Abstract: Presented herein are techniques for augmenting a speech recognition engine. According to the disclosed techniques, audio data is obtained as part of an automatic speech recognition session. Speech hints are also obtained as part of the automatic speech recognition session. A dynamic language model is generated from the speech hints for use during the automatic speech recognition session. A combined language model is then generated from the dynamic language model and a static language model. Finally, the audio data is converted to text using the combined language model as part of the automatic speech recognition session.