Standard automatic speech recognition (ASR) systems are increasingly expected to recognize foreign entities, yet doing so while preserving accuracy on native words remains a challenge. We describe a novel approach for recognizing foreign words by injecting them with appropriate pronunciations into the recognizer decoder search space on-the-fly. The pronunciations are generated by mapping pronunciations from the foreign language's lexicon to the target recognizer language's phoneme inventory. The phoneme mapping itself is learned automatically using acoustic coupling of Text-to-speech (TTS) audio and a pronunciation learning algorithm. Evaluation of our algorithm on Google Assistant use cases shows we can improve recognition of media-related queries by incorporating English entity pronunciations in French and German recognizers, with wins/losses ratios of roughly 2-3:1, without hurting recognition on general traffic.

We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.