Microsoft Technology Licensing, LLC

Tieyan Liu

Tao Qin

Di He

Shuxin Zheng

Jiang Bian

Guolin Ke

Sheng Zhao

Xu Tan

Junwei Gan

Yatao Li

According to implementations of the subject matter described herein, a solution for image rescaling is proposed. According to the solution, an input image of a first resolution is obtained. An output image of a second resolution and high-frequency information following a predetermined distribution are generated based on the input image by using a trained invertible neural network, where the first resolution exceeds the second resolution. Besides, a further input image of the second resolution is obtained. A further output image of the first resolution is generated based on the further input image and high-frequency information following the predetermined distribution by using an inverse network of the invertible neural network. This solution can downscale an original image into a visually-pleasing low-resolution image with the same semantics and also can reconstruct a high-resolution image of high quality from a low-resolution image.

Image rescaling

Implementations of the subject matter described herein provide a solution for speech synthesis and speech recognition. In this solution, a Text to Speech (TTS) model and an Automatic Speech Recognition (ASR) model supporting at least one language are obtained. The TTS model and the ASR model are adjusted, based on a first set of paired data in a target language, to support the target language. The TTS model is optimized based on the first set of paired data and a first set of synthesized paired data in the target language generated by the ASR model while the ASR model is optimized based on the first set of paired data and a second set of synthesized paired data in the target language generated by the TTS model. As such, the solution can provide TTS and ASR models with high accuracy for languages lacking training data by using less training data.

Speech synthesis and speech recognition

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Beijing, China

Tieyan Liu