1. A computer implemented method for identifying a direction of incoming speech input, the method comprising: during a configuration training period prior to a runtime period: identifying a plurality of training audio data feature vectors corresponding to audio captured using a first microphone array, determining, for each of the plurality of training audio feature vectors, first training data indicating that speech is represented in the respective training audio feature vector, determining, for each of the plurality of training audio feature vectors, a second training data indicating a respective microphone of the first microphone array corresponding to a strongest representation of speech represented in the respective training audio feature vector, and training a deep neural network (DNN) classifier using the training audio feature vectors, the respective first training indicators and the respective second training indicators, wherein the DNN classifier is configured to indicate whether speech is detected and what microphone's data has a highest signal quality; and during the runtime period after the configuration training period: detecting, by a device having a second microphone array comprising at least a first microphone and second microphone, audio, determining from the audio, by the device, a plurality of input feature vectors corresponding to a first audio frame, the plurality of input feature vectors including at least a first input feature vector corresponding to the first microphone and a second input feature vector corresponding to the second microphone, processing the plurality of input audio feature vectors using the DNN classifier to output: a confidence score that at least one of the plurality of input audio feature vectors represents speech, and a value indicating that the first input audio feature vector best represents the speech compared to other feature vectors of the plurality of input audio feature vectors.