Generic ASR Will Never be Accurate Enough for Conversational AI
Most conversational AI use cases don’t actually need text, they need intent. That is why there are challenges in providing ASR to conversational models. Was that speaker part of the conversation or were they just background that should be ignored? Should the model have transcribed ‘Hey Frank! Make sure pineapple is on there!’ or is that an error? A core issue is that ASR not only provides a transcript, it also decides things like what was part of the conversation and what wasn’t. Learn why one-size-fits-all ASRs lack understanding of intent and context and why a domain-targeted ASR is essential for Conversational AI.
Jeff "Susan" Ward is a Research Engineer at Deepgram, where for the past four years, he has been exploring and innovating technological solutions in the realm of automatic speech recognition. His work has focused on automating the entire training pipeline with the intent to enable rapid customization across a variety of ASR use cases. He also has experience in automatic alignment, transcript cleaning, large-scale data management, automated training, and model design. Before joining Deepgram, Susan earned his master's from the University of Edinburgh and his pilot wings from the US Navy.