Novel Deep Architectures in Speech Processing

Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, problem domain knowledge can be built into the constraints of the model. In addition, unsupervised inference tasks such as adaptation and clustering are handled in a natural way. However, these benefits typically come at the expense of difficulties during inference. In contrast, deterministic deep neural networks are constructed in such a way that inference is straightforward, and discriminative training is relatively easy. However, their typically generic architectures often make it unclear how to incorporate specific problem knowledge or to perform flexible tasks such as unsupervised inference. This chapter introduces frameworks to provide the advantages of both approaches. To do so, we start with a model-based approach and an associated inference algorithm, and reinterpret inference iterations as layers in a deep network, while generalizing the parametrization to create a more powerful network. We show how such frameworks yield new understanding of conventional networks, and how they can result in novel networks for speech processing, including networks based on nonnegative matrix factorization, complex Gaussian microphone array signal processing, and a network inspired by efficient spectral clustering. We then discuss what has been learned in recent work and provide a prospectus for future research in this area.