New Framework Uses Ontology Memory to Improve Speech Recognition in Long Conversations
Researchers have developed an ontology memory-augmented framework to correct automatic speech recognition (ASR) errors in long text-speech interleaved conversations. Traditional ASR correction methods struggle with long interactions because they lack sufficient contextual information to distinguish errors from noise. The approach organizes conversation history into a structured memory system that improves correction accuracy across most tested configurations.
A new ASR correction framework addresses a gap in speech recognition technology by handling the complexity of long, mixed text-speech conversations. Rather than relying on isolated utterances or raw dialogue history, the system creates a dynamically updatable ontology memory that stores entities, terminology, variants, potential ASR confusions, and semantic relationships as retrievable nodes. The researchers evaluated their method using RAMC-Corr, a new dataset derived from MAGIC-RAMC designed specifically for long-range ASR correction with grounded context. Experiments demonstrated improvements over direct correction methods in 9 out of 10 paired backbone-setting combinations, with the system producing more selective and evidence-grounded corrections for context-dependent errors. This work suggests that structured memory organization can help speech recognition systems better navigate the challenges of real-world conversational interactions.
What's missing
The paper does not discuss computational costs, latency implications, or scalability limitations of the ontology memory approach. Additionally, the generalizability of results beyond the RAMC-Corr dataset and performance on other language pairs or domains remains unclear from the abstract.
What different sources said
- arXiv cs.AICenter
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
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