5 accepted papers
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…
Accurate recognition of numeric sequences is crucial for many contextual speech recognition applications. For example, a user might create a calendar event and be prompted by a virtual assistant for the time, date, and duration of the event. We propose a modular and scalable solution for improved re…
Recurrent Neural Network Transducer (RNN-T) models [1] for automatic speech recognition (ASR) provide high accuracy speech recognition. Such end-to-end (E2E) models combine acoustic, pronunciation and language models (AM, PM, LM) of a conventional ASR system into a single neural network, dramaticall…
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 t…
The recognition of contact names in mobile-device voice commands is a challenging problem. Some of the difficulties include potentially infinite vocabularies, low probability of contact tokens in the language model (LM), increased false triggering of contact voice commands when none are spoken, and…