Part 7/13:

Addressing system architectures, the speaker compares traditional cascaded pipelines (speech recognition followed by language understanding and synthesis) with end-to-end models. Cascaded systems suffer from compounded errors—if each module is 80% accurate, the combined accuracy drops substantially, leading to less reliable outputs.

Modern research favors end-to-end speech models that directly convert audio to output, reducing error propagation and enabling more natural, coherent responses. Examples include OpenAI's GPT-4 voice modules and joint training strategies that combine multiple components into unified systems.

Challenges and Opportunities in Speech AI

The speaker highlights several open problems in speech AI: