audiohermit

Not really, this is the only thing I know of in terms of collection: https://www.isca-speech.org/archive/Interspeech_2018/pdfs/24... Usually you're basing your recipe off of those for existing datasets (TIMIT, WSJ, LibriSpeech, etc).

Agreed. I didn't have a better comparison at hand.

I'm looking at you GAN papers.

Much of the work in speech synthesis has been about closing the gap in vocoders, which take a generated spectrogram and output a waveform. There's a clear gap between practical online implementations and computational behemoths like WaveNet. As you implied it's hard to quantitatively judge which result is better, papers usually use surveys to judge.

Here's a recent work that has a good comparison of some vocoders: https://wavenode-example.github.io/

Edit: WaveRNN struck a good balance for me in the past but is not shown in the link. Tons of new work coming out though!

I'll push back on this. The quality of the read speech should be a higher concern than having parallel data. Unless OP's wife is a teacher or actor/voice actor, if LibriSpeech transcripts are boring, it will come out in the speech.

I think OP would ideally want the model to pick up on more natural intonation, instead of monotone dictation. Record everything from now on, as best you can with similar recording context, and hopefully that data will be enough to cover more natural nuances.

I work in pathological speech processing/synthesis so I'm unfortunately familiar with your father's position. It really sucks that these people didn't know that archiving their voice would've been useful. I hear snippets that people manage to glean from family videos right after listening to their current voices and it makes me really sad.

On the upside, your father can choose any celebrity he wants to voice him! Tons of celeb data is publicly available (VoxCeleb 1 & 2).

Hey, speech ML researcher here. Make sure you have different recordings of different contexts. fifteen.ai's best TTS voices use ~90 min of utterances, some separated by emotion. If you're having her read a text, make sure it's engaging--we do a lot of unconscious voicing when reading aloud. Tbh, if she has a non-Anglophone accent, you're going to need more because the training data is biased towards UK/US speakers.

If you want to read up on the basics, check out the SV2TTS paper: https://arxiv.org/pdf/1806.04558.pdf Basically you use a speaker encoding to condition the TTS output. This paper/idea is used all over, even for speech-to-speech translation, with small changes.

There's a few open-source version implementations but mostly outdated--the better ones are either private for business or privacy reasons.

There's a lot of work on non-parallel transfer learning (aka subjects are saying different things) so TTS has progressed rapidly and most public implementations lag a bit behind the research. If you're willing to grok speech processing, I'd start with NeMo for overall simplicity--don't get distracted by Kaldi.

Edit: Important note! Utterances are usually clipped of silence before/after so take that into account when analyzing corpus lengths. The quality of each utterance is much much more important than the length--fifteen.ai's TTS is so good primarily because they got fans of each character to collect the data.