How to augment speech content (likely usable as recognition augmentations too)

signal processing revealed

VTLP code: https://github.com/biggytruck/SpeechSplit2/blob/0911c09732e0e935c7c0a7aaf23eb2923d9889d8/utils.py#L252-L276

New SOTA on TTS from Microsoft Research Asia (outside of ICASSP)

Uses 24 hours (13100 utterances) from LJSpeech, 200M text sentences for phoneme encoder pretraining and a g2p model. 8 V100 GPUs. 3000 epochs.

https://speechresearch.github.io/naturalspeech/

In the mean time all Interspeech 2021 videos have been made available https://www.superlectures.com/interspeech2021/tutorials

https://www.youtube.com/channel/UC2-z0HD4WpSbJONj73BgfwQ/videos

https://www.youtube.com/watch?v=-p_awLZWLeI

https://github.com/facebookresearch/vocoder-benchmark

VocBench from Facebook

Autoregressive vocoders: WaveNet, WaveRNN
GANs: Parallel WaveGAN, MelGAN
Diffusion: WaveGrad, DiffWave

All in one place with a common input-output interface with modern codebase from Facebook.

Might be useful for VC if it’s easy to make condition those vocoders using custom features.

Neural HMM: learns alignments fast

https://shivammehta007.github.io/Neural-HMM/

Promises to converge with 500 utterances, i couldn’t get it to work with that much data. I think with 2k utterances it should.