Targeted Keyword Filtering for Accelerated Spoken Topic Identification

We present a novel framework for spoken topic identification that simultaneously learns both topic-specific keywords and acoustic keyword filters from only document-level topic labels. At inference time, only audio segments likely to contain topic-salient keywords are fully decoded, reducing the system’s overall computation cost. We show that this filtering allows for effective topic classification while decoding only 50% of ASR output word lattices, and achieves error rates within 1.2% and precision within 2.6% of an unfiltered baseline system.