Smart Hybrid System for Speaker Feel Recognition: Balance of Cost and Performance

Speech analytics have become one of the basic stones of modern customer service platforms. A new research released in ArXiv suggests an interesting solution on how to solve the feeling status of the event in the event of **

Research compares three different technological approaches:

• Light weight, low cost ensemble models that use embroidered embeddings and random forest algorithms Home
• Improved predictive capabilities with connective window technique Home
• Hybrid combination of classic machine learning methods such as logistic regression and XGBoost u
• selectively commissioning of large language models (with the mechanism of rotation-gating)
• Comparison of accuracy performance under cost and delay restrictions

Communication-center-as-a-service (CCaaS) platforms have critical importance. In order to support customer service agents, direct calls to the right departments, and then be analytical, the feeling recognition technology is necessary. However, it is not economically sustainable to run a big language model for each call.

Offered güven-door hybrid system, when simple models prefer to use them when they are successful, it is recommended to activate more powerful LLMs when uncertainty is higher. This approach provides optimal balance in accuracy and lag performance while lowering infrastructure costs. Research offers a practical example of how to design machine learning systems in real world applications.