various attributes of the synthesized speech. Attributes such as tone, accent, age, gender,
and more can be precisely controlled through in-context text guidance. This controllability
in TTS opens up exciting possibilities for personalization and customization, enabling users
to tailor the synthesized speech to their specific requirements. By leveraging advanced
models and techniques, researchers are making significant strides in developing controllable
TTS systems that provide users with a powerful and flexible speech synthesis experience.
(6) Parameter-efficient Learning: With the increasing scale of LLMs and speech models, it
becomes imperative to adapt these models with minimal parameter updates. This necessi-
tates the development of specialized adapters that can efficiently update these emerging
mixed-modality large models. Additionally, model compression techniques have proven
to be practical solutions in addressing the challenges posed by these large models. Recent
AReviewofDeepLearningTechniquesforSpeechProcessing