Mozilla to Represent Speech Synthesis System LPCNet

LPCNet uses DSP for LPC filtering (Linear Predictive Coding) and voice path modeling
21 November 2018   772

Mozilla reported on the new speech synthesis system LPCNet, which effectively translates text to speech while reducing resource demands. This is achieved through a combination of traditional digital signal processing methods (DSP, digital signal processing) with speech synthesis mechanisms based on a recurrent neural network.

The main problem of modern systems for real-time speech synthesis based on neural networks is high computational complexity. It does not allow to use them on smartphones and tablets.

LPCNet uses DSP for LPC filtering (Linear Predictive Coding) and voice path modeling. Then, instead of all the selected samples, the neural network receives only the forecast of each subsequent one. This frees the AI ​​from modeling the vocal tract and leaves it with only an adjustment to the problems in forecasting. Neural networks need only to monitor the accuracy of the forecast, and not to generate each sample in real time.

The technology can be used in other areas where you need to improve the quality of the voice signal. For example, for transmitting speech over low-speed communication channels, eliminating noise, filtering data and restoring fragments of speech lost during transmission.

LPCNet is written in C using a high-level framework for building Keras neural networks. A GTX 1080 Ti video card is desirable for operation. Ready-made models are available for download, but the system can be trained on your own data. LPCNet is distributed under the BSD license.

Mozilla's speech synthesis system is being developed as an alternative to WaveNet by Google. The WaveNet code was open to developers in March 2018.

Neural Network to Create Landscapes from Sketches

Nvidia created GauGAN model that uses generative-competitive neural networks to process segmented images and create beautiful landscapes from peoples' sketches
20 March 2019   328

At the GTC 2019 conference, NVIDIA presented a demo version of the GauGAN neural network, which can turn sketchy drawings into photorealistic images.

The GauGAN model, named after the famous artist Paul Gauguin, uses generative-competitive neural networks to process segmented images. The generator creates an image and transfers it to the discriminator trained in real photographs. He in turn pixel-by-pixel tells the generator what to fix and where.

Simply put, the principle of the neural network is similar to the coloring of the coloring, but instead of children's drawings, it produces beautiful landscapes. Its creators emphasize that it does not just glue pieces of images, but generates unique ones, like a real artist.

Among other things, the neural network is able to imitate the styles of various artists and change the times of the day and year in the image. It also generates realistic reflections on water surfaces, such as ponds and rivers.

So far, GauGAN is configured to work with landscapes, but the neural network architecture allows us to train it to create urban images as well. The source text of the report in PDF is available here.

GauGAN can be useful to both architects and city planners, and landscape designers with game developers. An AI that understands what the real world looks like will simplify the implementation of their ideas and help you quickly change them. Soon the neural network will be available on the AI ​​Playground.