Bipartite Graph Factorization in Static Decoding Graphs with Long-Span Acoustic Context

Explore the challenges and solutions in large vocabulary speech recognition through this lecture on bipartite graph factorization in static decoding graphs with long-span acoustic context. Delve into two standard approaches for searching the highest likelihood word sequence: on-demand construction and ahead-of-time full representation of the search space. Examine the problem arising from long-span acoustic context in full representation, focusing on bipartite graphs with O(V^2) edges in large vocabulary systems. Learn about the edge-wise minimal representation technique, involving the identification of complete bipartite sub-graphs and their replacement with extra vertices and connecting edges. Understand the NP-hard nature of finding the smallest representation and explore a heuristic approach for practical solutions. Gain insights from experimental results on a large-vocabulary speech recognition system and discuss related problems in the field.