VUI Response Entropy Matching for Disfluent Speaker Queries
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Solution Overview
Problem
Conventional Voice User Interfaces (VUIs) face challenges in effectively understanding and responding to users with speech disfluencies, such as 'hmm' and 'aah,' hesitation, and choice of words, particularly for Basic Emergent Users (BEUs) who speak low-resource languages, leading to inefficient communication.
Innovation Solution
An Information-entropy-based metric is employed to analyze spoken queries for disfluencies, pauses, and syllables, using a Large Language Model (LLM) to generate optimal text responses that match the user's communication style, ensuring the machine response is efficient and understandable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional VUI systems are used to interact with Basic Emergent Users (BEUs), then the system can process basic queries, but it fails to effectively understand and respond to speech disfluencies, hesitations, and non-standard language patterns
Solution Approach 1:
The system dynamically adapts its response characteristics based on the detected communication patterns of the user. By analyzing disfluencies, pauses, and speech characteristics in real-time, the VUI adjusts its response style, complexity, and pacing to match the user's communication ability, thereby improving both reliability of understanding and adaptability to individual users.
Solution Approach 2:
The system changes key parameters of the machine response including entropy level, response length, vocabulary complexity, and pacing based on the analyzed user speech characteristics. This parameter adaptation allows the VUI to effectively communicate with BEUs who exhibit various speech disfluencies and non-standard language patterns.
2Loss of information
If the VUI generates detailed and complex responses, then information completeness is improved, but the responses become difficult for BEUs to understand due to mismatched communication styles
Solution Approach 1:
The system applies different levels of information detail and linguistic complexity to different parts of the response based on the user's demonstrated communication ability. Critical information is delivered with appropriate emphasis and simplicity, while maintaining overall completeness. The response structure is locally adapted to match the user's understanding capacity.
Solution Approach 2:
The system uses the analyzed user speech patterns as feedback to adjust its response characteristics. By continuously monitoring disfluencies, pauses, and comprehension indicators in user responses, the VUI refines its information delivery style to maintain both completeness and understandability.
3Measurement precision
If the VUI analyzes multiple speech characteristics including disfluencies, pauses, and repetitions, then communication ability assessment is improved, but the system complexity increases
Solution Approach 1:
The speech analysis function is segmented into distinct modular components: disfluency detection, pause analysis, repetition identification, and entropy calculation. Each module processes specific speech characteristics independently and contributes to the overall communication ability assessment, making the complex analysis manageable and maintainable.
Solution Approach 2:
The speech analysis engine is designed as a universal module that handles multiple types of speech characteristics (disfluencies, pauses, repetitions, vocabulary choices) through a unified framework. This multi-functional approach assesses various communication patterns using consistent methods, reducing overall system complexity compared to separate specialized modules.
Data Source
AI summary
A method and system for Information-entropy-based metric for usable machine response of a Voice User Interface (VUI) to match with communication ability of a speaker is disclosed. The metric disclosed herein dynamically, on the fly analyses every received query for disfluencies such as ‘hmm’ and ‘aah,’ hesitation leading to pauses in speech, repetition, and vocabulary etc., to determine the property of the query in terms of communication ability or entropy in the query. A Large language Model (LLM) responding to the query is configured to generate and select and optimal response to the query such that efficiency of expression of the response to efficiency of expression of the query is minimal. Unlike the VUI analysis in the art, the interaction design for the VUI disclosed herein understands the mental model of the user (speaker) and communicate the system's response to the user in the user's language and communication style.