How to Search Audio and Transcripts Together

Audio Noise Reduction & Valid Signal Recognition
The AI algorithm first performs environmental noise reduction on original audio files. It filters steady background noise including indoor reverberation, distant ambient chatter, and low-frequency device buzzing. In standard conference environments, the system retains 98% of valid human voice signals while removing redundant noise waveforms.
This preprocessing ensures search keywords match clear voice segments instead of noise interference, laying a foundation for accurate audio positioning.

Voiceprint Distinction & Content Mapping Logic
The built-in voiceprint recognition model extracts unique frequency features of individual speakers. It can independently distinguish up to 4 speakers in a single multi-person conference scenario. The system binds each speaker’s voice segment to corresponding transcribed text in real time.
All text transcriptions, voiceprint-tagged audio clips, timestamp markers, manual notes, and snapshot images form a correlated data set. Every keyword search triggers full-data traversal across all associated content.
Practical Retrieval Capabilities
This integrated search mode breaks the limitation of single text retrieval. It enables synchronous query of original audio files, edited transcripts, key time markers, auxiliary recorded images, and text notes generated during recording.

All historical recording information forms a unified retrieval library. Any keyword input can quickly locate matching text paragraphs and corresponding audio segments, with bound images and notes displayed synchronously. This mechanism realizes efficient and comprehensive retrieval of all archived recording resources.