Abstract: Systems and processes for operating an intelligent automated assistant are provided. In one example, a method includes receiving mixed speech data representing utterances of a target speaker and utterances of one or more interfering audio sources. The method further includes obtaining a target speaker representation, which represents speech characteristics of the target speaker; and determining, using a learning network, probability distributions of phonetic elements directly from the mixed speech data. The inputs of the learning network include the mixed speech data and the target speaker representation. An output of the learning network includes the probability distributions of phonetic elements. The method further includes generating text corresponding to the utterances of the target speaker based on the probability distributions of the phonetic elements; and providing a response to the target speaker based on the text corresponding to the utterances of the target speaker.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. In one example, a method includes receiving mixed speech data representing utterances of a target speaker and utterances of one or more interfering audio sources. The method further includes obtaining a target speaker representation, which represents speech characteristics of the target speaker; and determining, using a learning network, probability distributions of phonetic elements directly from the mixed speech data. The inputs of the learning network include the mixed speech data and the target speaker representation. An output of the learning network includes the probability distributions of phonetic elements. The method further includes generating text corresponding to the utterances of the target speaker based on the probability distributions of the phonetic elements; and providing a response to the target speaker based on the text corresponding to the utterances of the target speaker.

Abstract: A system and method for applying a convolutional neural network (CNN) to speech recognition. The CNN may provide input to a hidden Markov model and has at least one pair of a convolution layer and a pooling layer. The CNN operates along the frequency axis. The CNN has units that operate upon one or more local frequency bands of an acoustic signal. The CNN mitigates acoustic variation.

Abstract: A system and method for applying a convolutional neural network (CNN) to speech recognition. The CNN may provide input to a hidden Markov model and has at least one pair of a convolution layer and a pooling layer. The CNN operates along the frequency axis. The CNN has units that operate upon one or more local frequency bands of an acoustic signal. The CNN mitigates acoustic variation.

Abstract: A system and method for applying a convolutional neural network (CNN) to speech recognition. The CNN may provide input to a hidden Markov model and has at least one pair of a convolution layer and a pooling layer. The CNN operates along the frequency axis. The CNN has units that operate upon one or more local frequency bands of an acoustic signal. The CNN mitigates acoustic variation.

Abstract: A system and method for applying a convolutional neural network (CNN) to speech recognition. The CNN may provide input to a hidden Markov model and has at least one pair of a convolution layer and a pooling layer. The CNN operates along the frequency axis. The CNN has units that operate upon one or more local frequency bands of an acoustic signal. The CNN mitigates acoustic variation.