Although end-to-end (E2E) automatic speech recognition (ASR) systems excel in general tasks, they frequently struggle with accurately recognizing personal rare words. Leveraging contextual information to bias the internal states of E2E ASR model has proven to be an effective solution. However most existing work focuses on biasing for a single domain and it is still challenging to expand such contextualization mechanisms to many domains. To address this limitation, in this work we propose a hierarchical attention architecture to scale contextual biasing to a wide range of domains simultaneously. Given multiple catalogs of contextual information, the high-level attention determines which source of catalog to focus on and the low-level attention learns to attend to the most relevant entity within the focused catalog. Experiments on diverse domains demonstrate the proposed architecture results in $35 \%$ to $60 \%$ relative WER improvements on personal rare words and outperforms existing approaches.

End-to-end (E2E) automatic speech recognition (ASR) models have been found to perform well on general transcription tasks but often fail to correctly recognize words that occur infrequently in the training data. Personalization is important for a variety of tasks, including virtual assistants where recall of infrequently observed words such as contact names, song titles and place names is critical. In these cases contextual information is often available which can be used to bias the E2E ASR model. Contextual biasing (CB) has been shown to be effective for this task, however most existing work focuses on biasing for a single domain and so in this work we focus on the application of biasing to multiple domains. We propose a method whereby the E2E ASR model is trained to emit opening and closing tags around slot content which are used to both selectively enable biasing and decide which catalog to use for biasing. Our method is shown to not only efficiently scale to multiple slots, but also further improves accuracy on slot content.

This paper proposes a method of speech enhancement where a clean speech signal is reconstructed from a sinusoidal model of speech production and a set of acoustic speech features. The acoustic features are estimated from noisy speech and comprise, for each frame, a voicing classification (voiced, unvoiced or non-speech), fundamental frequency (for voiced frames) and spectral envelope. Rather than using different algorithms to estimate each parameter, a single statistical model is developed. This comprises a set of acoustic models and has similarity to the acoustic modelling used in speech recognition. This allows noise and speaker adaptation to be applied to acoustic feature estimation to improve robustness. Objective and subjective tests compare reconstruction-based enhancement with other methods of enhancement and show the proposed method to be highly effective at removing noise.