Local Speaker Identification via Feature Extraction and Server Analysis
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Solution Overview
Problem
Automatic speaker identification poses challenges with limited computational resources and response time, particularly in call centers and mobile devices, where fast and reliable identification is crucial but resources are constrained.
Innovation Solution
A method for speaker identification that involves local detection and feature extraction, with optional encoding and transmission of features to a remote server for analysis, using techniques like vector quantization and differential vector quantization to reduce bandwidth and computational load, while employing confidence measures and outlier detection to enhance reliability and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If speaker identification is performed locally with limited computational resources, then device complexity is reduced, but identification reliability deteriorates
Solution Approach 1:
The system divides speaker identification into two segments: local feature extraction and remote server analysis. The local device extracts acoustic features and transmits them to a remote server for comprehensive analysis, allowing reliable identification without requiring high computational resources at the local device.
Solution Approach 2:
A remote server acts as an intermediary between the local device and the speaker identification database. The server receives extracted features, performs comprehensive comparison against stored speaker models, and returns identification results, enabling reliable identification while keeping local computational requirements minimal.
2Reliability
If comprehensive speaker identification analysis is performed, then identification reliability is improved, but response time deteriorates
Solution Approach 1:
The system performs preliminary feature extraction locally before transmission to the server. By pre-processing and extracting only the essential acoustic features (such as MFCCs) at the local device, the system reduces the amount of data to be transmitted and processed remotely, thereby decreasing overall response time while maintaining identification reliability.
Solution Approach 2:
The system extracts only the most relevant acoustic features from the speech signal for transmission to the server. By selecting and transmitting only essential features rather than the complete audio signal, the system minimizes transmission time and server processing requirements while preserving identification accuracy.
3Measurement precision
If speech activity detection threshold is lowered, then detection sensitivity is improved, but false positive rate deteriorates
Solution Approach 1:
The system uses feedback from the server's speaker identification results to validate local speech activity detections. The server compares extracted features against stored speaker models and provides feedback on whether detected speech segments correspond to known speakers, allowing the system to filter out false positives while maintaining high detection sensitivity.
Data Source
AI summary
Method for speaker identification includes detecting a target speaker's utterance locally; extracting features from the detected utterance locally, analyzing the extracted features in the local device to obtain information on the speaker identification and/or encoding the extracted features locally, transmitting the encoded extracted features to a remote server, decoding and analyzing the received extracted features by the server to obtain information on the speaker identification, and transmitting the information on the speaker identification from the server to the location where the speaker's utterance was detected. The method further includes detecting speech activity locally. Extracting features, encoding the extracted features, and/or transmitting the encoded extracted features to the server, are only performed if speech activity above some predetermined threshold is detected.