Machine Learning Classification for Ambiguous Speech Queries
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Solution Overview
Problem
Existing language understanding applications face challenges in effectively discriminating ambiguous requests due to reliance on costly and inefficient deterministic hard-coded rules, which lead to suboptimal user experiences and poor scalability for locale expansion.
Innovation Solution
The implementation of supervised and unsupervised machine learning techniques to dynamically discriminate ambiguous requests by processing natural language expressions through a dynamic system comprising a speech recognition component, language understanding component, and dialog component, which generates and ranks dialog hypotheses based on contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic hard-coded rules are used to determine when to use the digital assistant application or backend solution, then the system can provide structured decision-making, but the cost of crafting and implementing rules is high and accuracy evaluation is complex
Solution Approach 1:
The patent replaces the mechanical system of deterministic hard-coded rules with a machine learning-based classification system. The system uses trained models to automatically classify spoken language inputs into domains (e.g., calendar, contacts, web search) without requiring manual rule crafting. This substitution reduces implementation complexity while maintaining or improving decision accuracy through adaptive learning from data.
Solution Approach 2:
The system changes the parameters of decision-making from fixed rule-based thresholds to dynamic probability scores generated by machine learning models. The classification is based on confidence scores and probability distributions that can adapt to different locales and languages, reducing the need for manual parameter tuning and rule adjustments.
2Stability of the object's composition
If deterministic hard-coded rules are used for domain classification, then the system can provide consistent decision-making, but the rules do not scale well for locale expansion and new languages
Solution Approach 1:
The patent implements a universal machine learning classification system that handles multiple locales and languages through a single framework. The system is trained on diverse data from different regions and languages, enabling it to generalize across locales without requiring separate rule sets. This multi-functional approach maintains decision consistency while achieving broad locale scalability.
Solution Approach 2:
The system transitions from static hard-coded rules to dynamic machine learning models that can adapt to new locales and languages. The models are continuously trained on incoming data, allowing the system to evolve and expand to new regions without requiring manual rule updates. This dynamic approach maintains consistency through learned patterns while enabling versatile adaptation.
3Ease of manufacture
If the spoken language input is sent to the backend solution as is when using backend solution, then the implementation is simple, but the user experience is suboptimal
Solution Approach 1:
The patent applies preliminary processing to spoken language inputs before sending them to the backend solution. The machine learning classification system pre-processes the input by identifying the domain and generating appropriate queries or refinements. This preliminary action improves user experience by ensuring that backend queries are optimized and relevant, while maintaining implementation simplicity through automated preprocessing steps.
4Adaptability or versatility
If machine learning techniques are used to dynamically discriminate ambiguous requests, then the system provides better user experience and scalability, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the language understanding system into distinct components: speech recognition, machine learning classification, domain identification, and query generation. Each component handles a specific aspect of processing, reducing overall system complexity through modular design. The machine learning model focuses specifically on classification while other components handle their specialized tasks, making the system more manageable despite increased computational requirements.
Data Source
AI summary
Methods and systems are provided for discriminating ambiguous expressions to enhance user experience. For example, a natural language expression may be received by a speech recognition component. The natural language expression may include at least one of words, terms, and phrases of text. A dialog hypothesis set from the natural language expression may be created by using contextual information. In some cases, the dialog hypothesis set has at least two dialog hypotheses. A plurality of dialog responses may be generated for the dialog hypothesis set. The dialog hypothesis set may be ranked based on an analysis of the plurality of the dialog responses. An action may be performed based on ranking the dialog hypothesis set.