Analysis of the evolution and current state of Voice AI, particularly focusing on Automatic Speech Recognition (ASR) technologies like OpenAI's Whisper.
It traces the historical progression from early rule-based systems to the advent of deep learning and Transformer architectures, highlighting Whisper's innovation through large-scale, weakly supervised training and its impact on multilingual capabilities.
The document then examines the competitive landscape, comparing models from major players like Google and Meta and their diverse architectural approaches and strategic focuses.
Finally, it addresses the grand challenges and future directions of Voice AI, discussing critical issues such as hallucination mitigation, linguistic diversity, bias, accessibility, privacy concerns, and the transformative applications within high-stakes sectors like healthcare and finance, as well as creative industries.
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