Speaker-Calibrated Detection Using Individual Speech Features

The patent applies local quality by extracting and weighting speech features specific to each speaker rather than using uniform features for all speakers. The system identifies speaker-specific characteristics (such as particular phonemes, pitch contours, or spectral patterns) that are most discriminative for each individual, thereby improving identification accuracy without requiring a complete overhaul of the system architecture.

The patent employs parameter changes by dynamically adjusting the importance weights of different speech features based on speaker-specific calibration data. During the calibration phase, the system learns which features are most relevant for each speaker and modifies the feature weighting parameters accordingly, allowing the same base system to achieve high precision for different speakers through parameter adaptation rather than structural modification.

The patent applies preliminary action by implementing a calibration phase during which speaker-specific features are identified and stored before actual speaker detection begins. This upfront preparation work creates speaker profiles that capture individual characteristics, allowing the system to achieve high reliability during operational phase without repeatedly performing complex analysis. The calibration is performed once per speaker and then reused.

The patent uses copying by creating simplified speaker profile representations during calibration that capture essential speaker-specific characteristics. These profiles serve as compact copies or models of each speaker's acoustic properties, allowing the system to reference pre-computed feature importance weights and patterns during detection without performing full feature extraction and analysis for every comparison, thereby reducing operational complexity.