The field of speech and language processing is witnessing significant advancements, with a growing emphasis on developing more sophisticated and human-like models. Recent developments have focused on improving the accuracy and expressiveness of speech synthesis, as well as enhancing the ability of language models to understand and generate emotionally nuanced text. Notably, researchers are exploring the use of large language models and multimodal approaches to generate more realistic and engaging speech, as well as developing new datasets and evaluation frameworks to support the development of more effective speech and language processing systems.

Some noteworthy papers in this area include: The paper proposing LanStyleTTS, a non-autoregressive, language-aware style adaptive TTS framework, which enables fine-grained, phoneme-level style control across languages. The work introducing ELSA, a novel dataset for emotionally intelligent language generation, which comprises multiple emotionally nuanced variations of original sentences regenerated across distinct contextual styles. The Dopamine Audiobook system, a unified training-free approach for emotional and human-like audiobook generation and evaluation, which achieves superior emotional expression to state-of-the-art TTS models. The GOAT-TTS framework, an LLM-based text-to-speech generation approach optimized via a dual-branch architecture, which addresses fundamental tensions between acoustic characteristics, prompt speech-text pairs, and catastrophic forgetting of the LLM's native text comprehension.