Example-Based Voice Bot Training With Feature Emphasis
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Solution Overview
Problem
Existing voice bots are limited by predefined intent schemas, requiring extensive computational resources and memory to manually define and refine intent schemas, making them non-scalable and inefficient in handling the nuances of human speech.
Innovation Solution
A voice bot development platform that utilizes machine learning layers to train voice bots based on user-provided training instances, including feature emphasis inputs, to enhance accuracy and robustness without relying on extensive predefined schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional voice bot development is used, then deployment is simple, but customization capability is limited
Solution Approach 1:
The voice bot development platform is segmented into distinct modular components including speech-to-text conversion module, natural language understanding module, dialogue management module, and text-to-speech conversion module. Each module can be independently configured and trained, allowing developers to customize specific aspects of the voice bot without redesigning the entire system. This modular architecture enables flexible customization while maintaining manageable system complexity.
Solution Approach 2:
The platform provides universal training capabilities that can be applied across multiple voice bot scenarios and industries. The same core infrastructure supports diverse customization needs through configurable parameters, reusable training datasets, and adaptable dialogue templates. This multi-functionality allows a single platform to serve various applications from customer service to entertainment, reducing development complexity for customized solutions.
2Measurement precision
If voice bot training is performed without feature emphasis, then training process is simple, but response accuracy is insufficient
Solution Approach 1:
The platform implements feature emphasis mechanisms that allow selective enhancement of specific response qualities based on application needs. Developers can emphasize particular features such as accuracy, speed, or politeness in specific dialogue contexts without retraining the entire voice bot. This local quality adjustment enables precise control over response characteristics, improving accuracy where needed while maintaining overall training simplicity.
Solution Approach 2:
The training system allows dynamic adjustment of training parameters and emphasis weights to optimize response accuracy for different scenarios. By modifying parameters such as attention weights, learning rates, and feature priorities during training, the platform achieves high response accuracy without requiring complete retraining. This parameter-based control simplifies the training process while maintaining precision.
3Reliability
If comprehensive conversation context is used for training, then bot understanding is improved, but training data requirements increase
Solution Approach 1:
The platform extracts and utilizes only the most relevant contextual information from conversations for training purposes, rather than requiring comprehensive context. The dialogue management module identifies and extracts key entities, intents, and contextual relationships that are most important for understanding, reducing the volume of training data needed while maintaining reliable conversation understanding. This selective extraction approach balances understanding quality with data efficiency.
Data Source
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AI summary
Implementations are directed to providing a voice bot development platform that enables a third-party developer to train a voice bot based on training instance(s). The training instance(s) can each include training input and training output. The training input can include a portion of a corresponding conversation and a prior context of the corresponding conversation. The training output can include a corresponding ground truth response to the portion of the corresponding conversation. Subsequent to training, the voice bot can be deployed for conducting conversations on behalf of a third-party. In some implementations, the voice bot is further trained based on a corresponding feature emphasis input that attentions the voice bot to a particular feature of the portion of the corresponding conversation. In some additional or alternative implementations, the voice bot is further trained to interact with third-party system(s) via remote procedure calls (RPCs).