In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings;(2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals.

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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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