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(1) Large Speech Models: In addition to the advancements made with wav2vec2.0, further
progress in the field of ASR and TTS models involves the development of larger and more
comprehensive models, along with the utilization of larger datasets. By leveraging these
resources, it becomes possible to create TTS models that exhibit enhanced naturalness and
human-like prosody. One promising approach to achieve this is through the application of
adversarial training, where a discriminator is employed to distinguish between machine-
generated speech and reference speech. This adversarial framework facilitates the generation
of TTS models that closely resemble human speech, providing a significant step forward in
achieving more realistic and high-quality synthesized speech. By exploring these avenues,
researchers aim to push the boundaries of speech synthesis technology, ultimately enhancing
the overall performance and realism of TTS systems.
AReviewofDeepLearningTechniquesforSpeechProcessing