Conversation Agent Intent Detection via Contextual Keyword Matching

The speech processing system is segmented into distinct functional modules: audio signal reception module, intent detection module, and response generation module. The intent detection module is further divided into keyword matching submodule, utterance analysis submodule, and context interpretation submodule. This segmentation allows each module to specialize in specific tasks, improving detection reliability while managing system complexity through modular design.

An intermediary processing layer is introduced between audio signal reception and response generation. This intermediary module performs multiple analysis passes including keyword extraction, utterance normalization, and context matching before generating responses. This intermediate processing stage enhances detection reliability by thoroughly analyzing speech signals without requiring the entire system to be overly complex.

The system performs partial analysis by focusing on key discriminative features rather than comprehensive analysis of all speech signal characteristics. The intent detection module selectively extracts keywords and critical utterances that are most indicative of user intent, performing detailed analysis only on these partial elements. This approach achieves sufficient detection precision while significantly reducing computational resource consumption compared to full-signal analysis.

Different levels of analysis depth are applied to different portions of the speech signal based on their importance. Critical keywords and intent-bearing utterances receive deep analytical processing, while less significant portions receive minimal or no processing. This local quality approach optimizes the balance between detection precision and computational energy usage by concentrating resources where they provide the most value.

The analysis depth and methodology are dynamically adjusted based on the granularity level of the speech signal. The system automatically detects whether a speech input represents a broad response or an annotated response, and dynamically switches between lightweight keyword/utterance analysis for broad responses and more comprehensive intent analysis for annotated responses. This dynamic adaptation optimizes both energy efficiency and detection accuracy according to the specific characteristics of each input.

The system changes the parameter of analysis depth based on the detected granularity level of the speech signal. For broad responses with lower granularity, the system uses shallower analysis parameters (keyword and utterance level only). For annotated responses with higher granularity, the system increases analysis depth parameters to include full intent analysis. This parameter adaptation allows the system to match computational effort to the actual information content, reducing energy loss while maintaining appropriate detection accuracy.