Speech Disfluency Detection for Conversational Query Interpretation
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Solution Overview
Problem
Current speech recognition systems lack the ability to effectively interpret speech disfluencies, such as pauses and auditory fillers, which are crucial for demarcating phrase and title boundaries, leading to incomplete or inaccurate information retrieval during conversational interactions with machines.
Innovation Solution
The system detects and interprets speech disfluencies, using pauses and auditory fillers to determine user confidence and assist in formulating intent by providing suggestions or actively completing phrases, and compensates for clipping errors by analyzing the absence of pauses at the beginning and end of speech input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speech recognition systems ignore speech disfluencies to maintain processing speed, then processing efficiency is improved, but interpretation accuracy deteriorates
Solution Approach 1:
The system extracts speech disfluencies (pauses, fillers) from the speech stream as separate detectable events, then selectively processes them for interpretation purposes while maintaining efficient overall processing. This allows the system to separate the detection of disfluencies from the main speech recognition pipeline, improving both efficiency and accuracy.
Solution Approach 2:
The system performs preliminary detection and analysis of speech disfluencies during the speech input phase, before full interpretation is required. By identifying pauses and fillers upfront, the system can prepare confidence measures and alternative query inputs in advance, reducing later processing time while improving accuracy.
2Ease of operation
If the system actively completes phrases based on detected pauses, then user experience is improved, but system complexity increases
Solution Approach 1:
The system provides self-service by automatically detecting user intent through speech disfluencies and generating completions or alternative queries without requiring explicit user input. The system serves itself by using its own detection capabilities to improve the interaction, reducing the need for complex external processing while enhancing user experience.
Solution Approach 2:
The system uses feedback from detected speech disfluencies to adjust its interpretation and provide proactive completions. By monitoring pause patterns and confidence measures, the system feeds this information back into the query generation process, creating a responsive loop that improves ease of operation without requiring proportional increases in complexity.
3Measurement precision
If the system detects and processes all speech disfluencies, then interpretation accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial processing by focusing on the most significant speech disfluencies (such as prominent pauses or repeated fillers) rather than analyzing every minor speech variation. This selective approach captures the essential interpretation signals needed for accuracy while avoiding the time cost of processing all possible disfluencies exhaustively.
Data Source
AI summary
A method for using speech disfluencies detected in speech input to assist in interpreting the input is provided. The method includes providing access to a set of content items with metadata describing the content items, and receiving a speech input intended to identify a desired content item. The method further includes detecting a speech disfluency in the speech input and determining a measure of confidence of a user in a portion of the speech input following the speech disfluency. If the confidence measure is lower than a threshold value, the method includes determining an alternative query input based on replacing the portion of the speech input following the speech disfluency with another word or phrase. The method further includes selecting content items based on comparing the speech input, the alternative query input (when the confidence measure is low), and the metadata associated with the content items.