This paper presents advances in Google's hidden Markov model (HMM)-driven unit selection speech synthesis system. We describe several improvements to the run-time system; these include minimal latency, high-quality and fast refresh cycle for new voices. Traditionally unit selection synthesizers are limited in terms of the amount of data they can handle and the real applications they are built for. That is even more critical for reallife large-scale applications where high-quality is expected and low latency is required given the available computational resources. In this paper we present an optimized engine to handle a large database at runtime, a composite unit search approach for combining diphones and phrase-based units. In addition a new voice building strategy for handling big databases and keeping the building times low is presented.i , o l } refer to its observation vector o l and to a contextual model from

In this paper we present a novel Neural Architecture Search (NAS) framework to improve keyword spotting and spoken language identification models. Even with the huge success of deep neural networks (DNNs) in many different domains, finding the best network architecture is still a laborious task and very computationally expensive at best with existing searching approaches. Our search approach efficiently and robustly finds better model sequences with respect to hand-designed systems. We do this by constructing architectures incrementally, using a custom mutation algorithm and leveraging the power of parameter transfer between layers. We demonstrate that our approach can automatically design DNNs with an order of magnitude fewer parameters that achieves better performance than the current best models. It leads to significant performance improvements: up to 4.09% accuracy increase for language identification (6.1% if we allow an increase in the number of parameters) and 0.3% for phoneme classification in keyword spotting with half the size of the model.

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