Speech models fail in real conversations

The primary bottleneck for conversational AI isn't model architecture, but a fundamental mismatch in data distribution.

• –Standard training datasets assume clean turn-taking and stable language, which rarely happens in native multilingual or noisy environments.
• –Features like mid-sentence interruptions and overlapping speech are often treated as noise rather than core conversational data.
• –Code-switching (multilingualism) is a massive hurdle for models trained on monolingual silos.
• –This gap suggests that "scaling laws" alone won't solve real-world reliability without significantly noisier, more naturalistic datasets.
• –Developers are increasingly forced to build custom post-processing layers to handle what foundation models should handle natively.