Automated Speaker Spotting via Feature Extraction
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Solution Overview
Problem
Current speaker spotting methods require human listeners to manually sift through large collections of speech-based interactions to locate specific target speakers, which is inefficient due to the limited participation of target speakers in interactions, leading to excessive listening time.
Innovation Solution
A method and apparatus for automatically generating and matching speaker models based on speech samples, using probabilistic scoring and feature extraction to identify target speakers within multi-speaker interactions, allowing for efficient filtering and sorting of interactions containing the target speaker's speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual listening method is used to locate target speaker speech, then accuracy of speaker identification is maintained, but time consumption and productivity deteriorate significantly
Solution Approach 1:
The patent replaces the mechanical human listening process with an automated speaker spotting system that uses signal processing and pattern recognition algorithms. The system extracts features from speech signals, compares them against reference speaker profiles, and automatically identifies target speaker occurrences, eliminating the need for manual listening while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the speech recordings and the human listener. This intermediary system pre-processes the audio data, identifies potential target speaker segments, and presents only relevant results to human operators, thereby dramatically reducing the time required for speaker spotting while preserving accuracy through automated feature extraction and comparison.
2Productivity
If automated speaker spotting system is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent divides the speaker spotting system into distinct functional modules: speech signal acquisition, pre-processing, feature extraction, speaker profile creation, similarity computation, and result presentation. Each module performs a specific task and can be independently developed, tested, and optimized, thereby managing system complexity while achieving high productivity through automated processing.
3Measurement precision
If feature extraction and speaker modeling are performed, then accuracy of target speaker detection is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent extracts only the most discriminative features from speech signals (such as pitch, formants, spectral characteristics) rather than processing the entire signal. By selecting and extracting only the relevant acoustic features that best distinguish between speakers, the system achieves high detection accuracy while minimizing computational resource consumption and processing time.
Data Source
AI summary
A method and apparatus for spotting a target speaker within a call interaction by generating speaker models based on one or more speaker's speech; and by searching for speaker models associated with one or more target speaker speech files.