Speech Recognition System Using Semantic Delimiters to Reduce False Positives
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Solution Overview
Problem
Current communication systems, particularly in VoIP, face challenges in creating a lifelike conversation experience with software agents due to false recognition of speech disfluencies and background noise, leading to disjointed interactions.
Innovation Solution
The system employs Automatic Speech Recognition (ASR) to identify complete sentences and uses a language model to determine confidence values, generating responses only when a grammatically complete sentence is detected, thereby reducing false positives and enhancing conversational realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system continuously generates responses to partial word sets, then responsiveness and engagement improve, but false recognition of speech disfluencies and background noise increases
Solution Approach 1:
The system performs preliminary actions by continuously identifying individual words and building word sets as speech is received, preparing potential responses in advance. However, it delays final response generation until a semantic delimiter is detected, ensuring accuracy before committing to a response.
Solution Approach 2:
The system uses feedback from semantic delimiter detection to control response generation. The language model continuously evaluates word sets and provides feedback on whether a complete semantic unit has been formed, allowing the system to adjust its response timing and accuracy accordingly.
2Reliability
If the system waits for complete sentences before responding, then accuracy improves, but conversational naturalness and responsiveness deteriorate
Solution Approach 1:
The system performs preliminary processing of incoming speech by continuously identifying words and building word sets, preparing potential responses in advance. This allows the system to respond quickly once a semantic delimiter is detected, maintaining both accuracy and responsiveness.
Solution Approach 2:
The system dynamically adjusts its response timing based on the detection of semantic delimiters. Rather than using a fixed threshold, it flexibly determines when a complete semantic unit has been formed, allowing responsive interaction while maintaining accuracy.
3Productivity
If the system processes every word set as a potential response, then engagement improves, but computational complexity and processing overhead increase
Solution Approach 1:
The system extracts only the essential information needed for response generation by focusing on semantic delimiters rather than processing every possible word set. This reduces computational complexity while maintaining engagement by responding to meaningful speech units.
Solution Approach 2:
The language model acts as an intermediary between raw speech input and response generation. It processes word sets and identifies semantic delimiters, filtering out unnecessary processing of incomplete or disfluent speech while maintaining engagement with meaningful input.
4Productivity
If the system responds to partial speech input, then responsiveness improves, but false positives from disfluencies and background noise increase
Solution Approach 1:
The language model provides continuous feedback by evaluating each word set to determine if a semantic delimiter is present. This feedback mechanism allows the system to maintain responsiveness while filtering out false positives from disfluencies and background noise through intelligent semantic analysis.
Solution Approach 2:
The system changes the parameter for response triggering from fixed time-based thresholds to semantic-based delimiters. This allows responsive interaction while significantly reducing false positives by base( responses on meaningful linguistic structures rather than arbitrary timing criteria.
Data Source
AI summary
Methods and systems for speech recognition are disclosed. In one aspect, a method includes receiving an input signal representing spoken words, identifying individual words in the input signal, building a plurality of word sets based on the individual words, generating a partial result based on at least one of the plurality of word sets, receiving an additional input signal subsequent to the generation of the partial result, identifying an additional word from the additional input, detecting a semantic delimiter based on at least one of the plurality of word sets and the additional word, and in response to the detection, generating a response to the input signal based at least in part, on the partial result and the semantic delimiter.