  Pitch estimation is an essential step of many speech processing algorithms,
including speech coding, synthesis, and enhancement. Recently, pitch estimators
based on deep neural networks (DNNs) have have been outperforming
well-established DSP-based techniques. Unfortunately, these new estimators can
be impractical to deploy in real-time systems, both because of their relatively
high complexity, and the fact that some require significant lookahead. We show
that a hybrid estimator using a small deep neural network (DNN) with
traditional DSP-based features can match or exceed the performance of pure
DNN-based models, with a complexity and algorithmic delay comparable to
traditional DSP-based algorithms. We further demonstrate that this hybrid
approach can provide benefits for a neural vocoding task.
