Abstract: Novel methods and systems for adapting a voice cloning synthesizer for a new speaker using real speech data are disclosed. Utterances from one or more target speakers are parameterized and are used to initialize an embedding vector for use with a voice synthesizer, by means of clustering the utterance data and determining the centroid of the data, using a speaker identification neural network, and/or by finding the closest stored embedded vector to the utterance data.

Abstract: A system for suppressing noise and enhancing speech and a related method are disclosed. The system trains a neural network model that takes banded energies corresponding to an original noisy waveform and produces a speech value indicating the amount of speech present in each band at each frame. The neural model comprises a feature extraction block that implements some lookahead. The feature extraction block is followed by an encoder with steady down-sampling along the frequency domain forming a contracting path. The encoder is followed by a corresponding decoder with steady up-sampling along the frequency domain forming an expanding path. The decoder receives scaled output feature maps from the encoder at a corresponding level. The decoder is followed by a classification block that generates a speech value indicating an amount of speech present for each frequency band of the plurality of frequency bands at each frame of the plurality of frames.

Abstract: Computer-implemented methods for speech synthesis are provided. A speech synthesizer may be trained to generate synthesized audio data that corresponds to words uttered by a source speaker according to speech characteristics of a target speaker. The speech synthesizer may be trained by time-stamped phoneme sequences, pitch contour data and speaker identification data. The speech synthesizer may include a voice modeling neural network and a conditioning neural network.

Abstract: A speech separation server comprises a deep-learning encoder with nonlinear activation. The encoder is programmed to take a mixture audio waveform in the time domain, learn generalized patterns from the mixture audio waveform, and generate an encoded representation that effectively characterizes the mixture audio waveform for speech separation.

Abstract: Novel methods and systems for adapting a voice cloning synthesizer for a new speaker using real speech data are disclosed. Utterances from one or more target speakers are parameterized and are used to initialize an embedding vector for use with a voice synthesizer, by means of clustering the utterance data and determining the centroid of the data, using a speaker identification neural network, and/or by finding the closest stored embedded vector to the utterance data.

Abstract: Computer-implemented methods for speech synthesis are provided. A speech synthesizer may be trained to generate synthesized audio data that corresponds to words uttered by a source speaker according to speech characteristics of a target speaker. The speech synthesizer may be trained by time-stamped phoneme sequences, pitch contour data and speaker identification data. The speech synthesizer may include a voice modeling neural network and a conditioning neural network.