Abstract: Examples relate to on-device non-semantic representation fine-tuning for speech classification. A computing system may obtain audio data having a speech portion and train a neural network to learn a non-semantic speech representation based on the speech portion of the audio data. The computing system may evaluate performance of the non-semantic speech representation based on a set of benchmark tasks corresponding to a speech domain and perform a fine-tuning process on the non-semantic speech representation based on one or more downstream tasks. The computing system may further generate a model based on the non-semantic representation and provide the model to a mobile computing device. The model is configured to operate locally on the mobile computing device.

Abstract: Embodiments of the present invention provide systems, methods and computer program products for testing software in a virtual private environment. One embodiment of a method for testing software in a virtual private environment includes cloning an original computing environment into one or more virtual environments, wherein the one or more virtual environments have identical configurations to the original computing environment and contain one or more client applications. The method further comprises determining a private network IP address corresponding to a registration request from an end user and establishing a connection between the end user and the virtual private network. The private network IP address is routed to a first of one or more identical virtual environments. Access is then allowed to the first virtual environment in order to allow the end user to test the one or more client applications on the first virtual environment.