DiffWave [269], HiFi-GAN [268], StyleMelGAN [386], Fre-GAN [254]

iSTFTNet [241], Avocodo [32]

Parallel Tacotron2 [126], Grad-TTS [431], VITS [251]
RAD-TTS [493], WaveGrad2 [69], DelightfulTTS [342]

PortaSpeech [459], DiffGAN-TTS [340], JETS [318]
WavThruVec [502], FastDiff [204], CLONE [343]

5.2.2 Datasets
The field of neural speech synthesis is rapidly advancing and relies heavily on high-quality datasets
for effective training and evaluation of models. One of the most frequently utilized datasets in
this field is the LJ Speech [217], which features about 24 hours of recorded speech from a single
female speaker reading passages from the public domain LJ Speech Corpus. This dataset is free
and has corresponding transcripts, making it an excellent choice for text-to-speech synthesis tasks.
Moreover, it has been used as a benchmark for numerous neural speech synthesis models, including
Tacotron [583], WaveNet [402], and DeepVoice [18, 156].
AReviewofDeepLearningTechniquesforSpeechProcessing