Who should jump in now: conversational agents and interactive voice interfaces
If your product involves a back-and-forth conversation—think customer support, virtual assistants, or embodied characters—you're the prime candidate. The latest models are built specifically for this use case: a 2024 system called GPT-Talker transforms multi-turn dialogue history into token sequences to predict both what to say and how to say it, and it outperformed other state-of-the-art systems in naturalness and expressiveness [1]. That means your bot can sound genuinely empathetic, not just read a script.
The data barrier is also lower than you might fear. A 2026 model, SA-SLM, trained on only 800 hours of expressive speech—a relatively small dataset—surpassed all open-source baselines and came within 0.08 points of GPT-4o-Audio on the EchoMind expressiveness benchmark [2]. For a team with access to even a few hundred hours of natural conversational audio, the payoff is real: your users will perceive more engagement and less robotic flatness.
Who should wait: high-stakes and low-resource domains
If your application is in a domain where a mispronounced or emotionally off-base response could have serious consequences—like medical advice, legal information, or emergency services—you should hold off until the technology matures. The 2021 handbook on expressive speech synthesis cautions that designing for social interaction requires careful consideration of context and user perception [3]. Current models still struggle with the 'semantic understanding-acoustic realization gap,' where the model understands the intent but fails to deliver it in the audio [2]. In high-stakes settings, that gap could erode trust or cause misunderstandings.
Similarly, if you're working with a low-resource language or a niche domain with little existing conversational data, wait. The 2024 GPT-Talker paper explicitly notes that small-scale datasets with scripted recordings fail to simulate real natural conversational styles [1]. They had to build a 236-hour dataset (NCSSD) spanning Chinese and English, including improvised speech and TV dialogues, to get good results. If you can't assemble that kind of data, your expressive model will likely sound flat or unnatural, defeating the purpose.
What makes the difference: data quality and self-awareness
The key condition for success is having natural, conversational training data—not scripted studio recordings. The 2024 study found that scripted styles are a major limitation, which is why they created a dataset with improvised and TV-show dialogues [1]. If your team can gather or license such data, you're in a good position to benefit now.
Another factor is whether the model can 'self-critique' its own output. The 2026 SA-SLM model uses a mechanism where the model acts as its own critic to verify that the audio matches the intended expression, which is what allowed it to achieve near-GPT-4o-Audio quality with only 800 hours of data [2]. Teams that can adopt such self-aware architectures will see faster gains than those using older, less feedback-driven systems.
About These Sources
This answer is built on 3 studies (2 peer-reviewed, 1 preprint) — published from 2021 to 2026, 2 from 2024 or later — selected as the most relevant from 3 studies that passed quality screening, drawn from 41 papers retrieved from a database of over 500 million.
Sources used in this answer
Generative Expressive Conversational Speech Synthesis
GPT-Talker, a generative conversational speech synthesis system, outperformed state-of-the-art models in naturalness and expressiveness, but required a new 236-hour natural conversational dataset (NCSSD) because small scripted datasets were insufficient.
Bridging What the Model Thinks and How It Speaks: Self-Aware Speech Language Models for Expressive Speech Generation
SA-SLM, a 3B-parameter self-aware speech language model trained on only 800 hours of expressive speech, surpassed all open-source baselines and came within 0.08 points of GPT-4o-Audio on the EchoMind benchmark, addressing the semantic understanding-acoustic realization gap.
Building and Designing Expressive Speech Synthesis
A 2021 handbook chapter on expressive speech synthesis emphasizes the importance of design considerations for social interaction, noting that expressive speech must be carefully crafted for context and user perception, implying that not all applications are ready for automatic expressive generation.