A funicular window can map recognized phrases to a tiny reply table or send each line through a pretrained language model, then speak. How should that comparison be designed?
Select an answer to reveal the explanation.
Short Explanation
Table versus language-model brain is still one comparison: same shoppers’ lines in, same voice out. Hold the spoken test set and TTS fixed so the only difference is how understanding writes the reply.
Full Explanation
Comparing a rigid NLP reply table with a pretrained LLM text arm inside a Riva pipeline requires fixed evaluation speech and a fixed TTS voice. That keeps the experiment about understanding behavior, not about ASR drift or synthesis changes. GENL app-framework bake-offs and image metrics are the wrong framing.