International Business Machines Corporation

Nuance Communications, Inc.

Raul Fernandez

Asaf Rendel

Michael Alan Picheny

Ellen Marie Eide

Andrew M Rosenberg

Alexander Sorin

Wael Mohamed Hamza

A computer system receives a training set of voice data captured from one or more speakers and classified with one or more categorical prosodic labels according to one or more prosodic categories. The computer system transforms the voice data of the training set while preserving one or more portions of the voice data that determine the one or more categorical prosodic labels to automatically form a new training set of voice data automatically classified with the one or more categorical prosodic labels according to the one or more prosodic categories. The computer system augments a database comprising the training set with the new training set for training a speech model of an artificial intelligence system.

Voice-transformation based data augmentation for prosodic classification

Systems and methods for prosody prediction include extracting features from runtime data using a parametric model. The features from runtime data are compared with features from training data using an exemplar-based model to predict prosody of the runtime data. The features from the training data are paired with exemplars from the training data and stored on a computer readable storage medium.

Hybrid predictive model for enhancing prosodic expressiveness

Systems and methods for dynamically selecting among text-to-speech (TTS) systems. Exemplary embodiments of the systems and methods include identifying text for converting into a speech waveform, synthesizing said text by three TTS systems, generating a candidate waveform from each of the three systems, generating a score from each of the three systems, comparing each of the three scores, selecting a score based on a criteria and selecting one of the three waveforms based on the selected of the three scores.

System and method for dynamically selecting among TTS systems

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Raul Fernandez