Speaker-Profile Speech Recognition for Multi-Speaker Commands
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Solution Overview
Problem
Conventional speaker identification and speech recognition algorithms struggle in multiple speaker environments due to insufficient data and poor training, failing to effectively separate and recognize speech from different speakers and background noise.
Innovation Solution
A system and method for recognizing speech that includes segmenting audio signals based on speaker profiles, determining confidence and error scores, and executing commands only when scores meet specific thresholds, utilizing a virtual assistant with modules for speaker identification, speech recognition, and environmental data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speaker identification and speech recognition algorithms are used in multiple speaker environments, then the system can process audio signals, but the accuracy and reliability of speech recognition deteriorates due to insufficient data and poor training
Solution Approach 1:
The patent segments the audio signal into multiple speaker components using source separation techniques. This allows the system to isolate individual speaker speech from mixed audio streams, enabling accurate speaker identification and speech recognition even in multi-speaker environments where conventional algorithms fail.
Solution Approach 2:
The system performs preliminary speaker identification and audio segmentation before speech recognition. By pre-processing the audio to separate speaker components and identify speakers in advance, the system prepares clean, organized data that improves the reliability of subsequent speech recognition operations.
2Productivity
If the system processes all audio signals without segmentation, then the processing speed is maintained, but the ability to isolate and recognize speech from different speakers deteriorates
Solution Approach 1:
The patent implements real-time audio segmentation that divides mixed audio streams into separate speaker components. This segmentation enables the system to maintain processing efficiency while preserving speaker-specific information, as each speaker's speech can be independently analyzed without manual intervention.
Solution Approach 2:
The system automatically performs speaker identification and audio segmentation without requiring manual processing. The automated algorithms continuously analyze and separate speaker components in real-time, maintaining high processing speed while preventing loss of speaker-specific information through intelligent, self-managing audio analysis.
3Loss of time
If the system executes commands without confidence scoring, then the response time is reduced, but the error rate in command execution increases
Solution Approach 1:
The patent implements a confidence scoring mechanism that provides feedback on the reliability of speaker identification and speech recognition results. The system calculates confidence scores based on the quality of audio segmentation and speaker identification, allowing it to make informed decisions about command execution while maintaining appropriate response times.
Solution Approach 2:
The system dynamically adjusts the threshold for command execution based on confidence scores. When confidence is high, commands are executed quickly; when confidence is low, the system can request clarification or delay execution. This parameter-based approach balances response time with execution accuracy.
Data Source
AI summary
Systems, methods, and computer readable media comprising instructions executable by a processor, for recognizing speech within a received audio signal segment the audio signal to isolate the speech based on a speaker audio profile, determine from the audio signal a command, a first score reflecting confidence in determining the command, and a second score reflecting a potential error in determining the command, and cause the command to be executed if the first score is above a first threshold value and the second score is below a second threshold value.