Voice-to-text services often fail at transcribing voicemails not because of compute limitations, but because they don't use context. They process audio in a vacuum, failing to recognize the recipient's name or other contextual clues that a human—or a smarter AI—would use for accurate interpretation.
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Descript's AI audio tool worsened after they trained it on extremely bad audio (e.g., vacuum cleaners). They learned the model that best fixes terrible audio is different from the one that best improves merely "okay" audio—the more common user scenario. You must train for your primary user's reality, not the worst possible edge case.
Using AI to generate content without adding human context simply transfers the intellectual effort to the recipient. This creates rework, confusion, and can damage professional relationships, explaining the low ROI seen in many AI initiatives.
While Genspark's calling agent can successfully complete a task and provide a transcript, its noticeable audio delays and awkward handling of interruptions highlight a key weakness. Current voice AI struggles with the subtle, real-time cadence of human conversation, which remains a barrier to broader adoption.
Success for dictation tools is measured not by raw accuracy, but by the percentage of messages that are perfect and require no manual correction. While incumbents like Apple have a ~10% 'zero edit rate,' Whisperflow's 85% rate is what drives adoption by eliminating the friction of post-dictation fixes.
While AI efficiently transcribes user interviews, true customer insight comes from ethnographic research—observing users in their natural environment. What people say is often different from their actual behavior. Don't let AI tools create a false sense of understanding that replaces direct observation.
When a prospect's voicemail directs you to text, structure your message for reading, not listening. Start with relevance about them, not your name, because they will likely read a transcript. This optimizes the message for the medium they've chosen.
A non-obvious failure mode for voice AI is misinterpreting accented English. A user speaking English with a strong Russian accent might find their speech transcribed directly into Russian Cyrillic. This highlights a complex, and frustrating, challenge in building robust and inclusive voice models for a global user base.
AI models lack access to the rich, contextual signals from physical, real-world interactions. Humans will remain essential because their job is to participate in this world, gather unique context from experiences like customer conversations, and feed it into AI systems, which cannot glean it on their own.
The magic of ChatGPT's voice mode in a car is that it feels like another person in the conversation. Conversely, Meta's AI glasses failed when translating a menu because they acted like a screen reader, ignoring the human context of how people actually read menus. Context is everything for voice.
ElevenLabs found that traditional data labelers could transcribe *what* was said but failed to capture *how* it was said (emotion, accent, delivery). The company had to build its own internal team to create this qualitative data layer. This shows that for nuanced AI, especially with unstructured data, proprietary labeling capabilities are a critical, often overlooked, necessity.
