Deep learning performs speech recognition by training millions of weights through backpropagation on massive datasets. A biologically inspired alternative — Assembly Calculus — now performs the same task with no weight training at all.
The system converts speech into spike patterns via probabilistic mel binarization and population-coded MFCCs, then processes them through hierarchical neuronal assemblies governed by Hebbian plasticity and winner-take-all competition. No backpropagation. No gradient descent. No dataset larger than the signal itself. Phone boundaries detected at F1=0.69; word boundaries at F1=0.61; phone recognition at 47.5%.
The numbers aren't competitive with state-of-the-art deep learning. That's not the point. The point is that they're nonzero. A dynamical system with no learned parameters, using only local plasticity rules and competitive inhibition, can segment continuous speech into phonemes and words. The capacity to parse speech is not in the weights — it's in the architecture. Hebbian association and winner-take-all are sufficient mechanisms.
This inverts the standard assumption about what learning contributes. Deep learning treats architecture as scaffolding and weights as the intelligence. Assembly Calculus treats architecture as the intelligence and weights as unnecessary. Neither extreme is the full story, but the existence of a working zero-training baseline reveals that the architectural contribution is larger than the deep learning paradigm suggests.