Enhanced Solution for Intent Recognition in Customer Service Scenarios
A hybrid enhancement strategy is recommended when building a customer service system based on Kimi-Audio:
- Context Memory Optimization: in
generate()current settingmemory_window=10Retain the history of the last 10 rounds of dialogues and adoptintent_keywordsParameter Injection Domain Terminology - Joint multi-task learning: Synchronize calls to the AQA and ASR modules using the
vote_weight={"asr":0.3, "aqa":0.7}Weighted consolidated results - Adjustment of dynamic discourse: Real-time switching of response templates based on SER results, e.g. detection of
angerTriggering a branch of soothing speech when emotional
Implementation Steps:
1. UtilizationKimi-Audio-Evalkit(used form a nominal expression)customer_serviceDataset for baseline testing
2. Inconfig/intent_patterns.yamlConfigure business-related regular expressions in the
3. Adoptiondocker-compose scale worker=3Enabling parallel request processing
Typical error handling scheme: when there are 3 consecutive low confidence (<0.6), switch to manual while recording anomalous audio for subsequent model fine-tuning.
This answer comes from the articleKimi-Audio: Open Source Audio Processing and Dialogue Base ModelingThe
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