With neural machine translation (NMT) and advanced speech recognition models continuously improving, the quality gap between artificial intelligence (AI) and human translation is narrowing in certain contexts. Still, current AI-powered speech translation capabilities remain frequently ineffective for complex, nuanced content.

While advances in context-aware AI models and retrieval-augmented generation (RAG) are showing promise, many AI speech translation tools still have limited contextual memory, which increases the risk of hallucinations and inaccuracies. Even with the expansion of such memory, consistency remains a challenge.

Fully accurate AI speech translation would require artificial general intelligence (AGI), which current AI technology — including large language models (LLMs) — does not have. The real-time nature of AI speech translation during events only intensifies complexity, especially for LLMs, which deliver best results on input that is complete and final; this is never the case with real-time human speech.

This does not mean that AI is not capable of accurate audio translations in situations where the context is straightforward or correctly determined by AI. In fact, for much of standardized business content, presentations with established terminology, or speeches with clear structure, AI translation quality can rival human performance. But for high-stakes, culturally sensitive, or highly technical events, human interpretation remains vital.

The Ideal Hybrid Intelligence Model

The collaboration of human staff and AI-powered interpretation ensures that the full message behind every event presentation is consistently delivered, without compromise. Such a model also enables real-time quality monitoring, where AI can flag potential issues for human review and humans can provide feedback that improves AI performance over time.

A truly multilingual event across a packed agenda should remain seamless throughout. Some interpretations should be handled by humans, while others can be covered by AI speech translation. As AI models evolve to better handle specialized terminology, cultural nuances, and speaker idiosyncrasies, the division of labor between humans and AI will become increasingly dynamic and optimized.

This approach not only leaves room for customization based on demand among the attendee base, but also — by trusting in a cloud-based remote simultaneous interpretation platform — enables the smooth integration of human interpreters and AI speech translation. This becomes a win-win scenario that delivers high-quality interpretation and live translation at scale, at a price point that doesn’t blow the budget.

Balancing Affordability and Inclusion

AI has given event organizers more choice and can deliver more language translation options for audience members and event attendees. For example, the cost-efficiency of AI enables organizations to offer multilingual access for smaller sessions and breakout rooms that previously wouldn’t justify the expense of human interpreters.

However, when evaluating AI-only platforms, event organizers should always test the AI speech translation and translated captions on content that is similar to the content of the planned event, as well as for longer periods of time — since many AI tools suffer from deterioration of quality over time or are not able to handle faster speech. Establishing clear quality benchmarks can help organizations ensure that expectations can be met before going live with the solution.

There is never a single solution that fits all events. Certain types of events, topics, or speakers may be more or less suitable for AI-based speech translation, and audience expectations from event to event can differ a lot. This, combined with the risk of spending missteps if a solution is found to be unsuitable, underscores the importance of trying before you buy.

The Road Ahead

The trajectory of AI development suggests that translation quality will continue to improve over the coming years, particularly as models gain better real-time processing capabilities and deeper understanding of context, tone, and cultural nuances. But until we reach the stage where AGI is realized and made readily available, the availability of human interpretation experts remains vital.

For the best results, determining where AI-powered speech translation is required and pairing that with experienced interpreters can go a long way to scaling inclusive events, while significantly reducing the costs of multilingual capabilities. As the technology matures, organizations that invest in hybrid approaches today will be positioned to adapt and optimize their multilingual strategies for tomorrow.