Abstract: The present disclosure relates to an apparatus for image recognition. The apparatus comprises a machine learning network configured to map first and second input image data to either a first or a second predefined target probability distribution, depending on whether the first and second input image data correspond to matching or non-matching images, wherein an output of the machine learning network matching the first target probability distribution is indicative of matching images and an output of the machine learning network matching the second target probability distribution is indicative of non-matching images. The present disclosure also relates to a method for training the apparatus for image recognition.

Abstract: Control over speaking style of a text-to-speech (TTS) system is provided without necessarily requiring that the training of the TTS conversion process (e.g., the ANN used for the conversion) take into account the speaking styles of the training data. For example, the TTS system may allow adjustment of characteristics of speaking styles, such as, speed, perceivable degree of “kindness”, average pitch, pitch variation, and duration of pauses. In some examples, a voice designer may have a number of independent controls that vary corresponding characteristics without necessarily varying others. Once the designer has configured a desired overall speaking style based on those controllable characteristics, the TTS system can be configured to use that speaking style for deployments of the TTS system. For example, the TTS system may be used for audio output in a voice assistant, for instance, for an in-vehicle voice assistant.

Abstract: Disclosed are systems, methods and other implementations for speech generation, including a method that includes obtaining a speech sample for a target speaker, processing, using a trained encoder, the speech sample to produce a parametric representation of the speech sample for the target speaker, receiving configuration data for a speech synthesis system that accepts as an input the parametric representation, and adapting the configuration data for the speech synthesis system according to an input comprising the parametric representation, and a time-domain representation for the speech sample, to generate adapted configuration data for the speech synthesis system.

Abstract: The present disclosure relates to an apparatus for image recognition. The apparatus comprises a machine learning network configured to map first and second input image data to either a first or a second predefined target probability distribution, depending on whether the first and second input image data correspond to matching or non-matching images, wherein an output of the machine learning network matching the first target probability distribution is indicative of matching images and an output of the machine learning network matching the second target probability distribution is indicative of non-matching images. The present disclosure also relates to a method for training the apparatus for image recognition.