Speaker Audio Embedding Vector Generation for Voice Isolation
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Solution Overview
Problem
Existing voice call technologies face challenges in isolating main speaker audio from parasitic speaker audio and noise audio, leading to poor user experience and increased computational resource usage.
Innovation Solution
The implementation of embedding generator circuitry that automatically generates a personal embedding vector for the main speaker without an enrollment process, iteratively updating it with new audio data and a universal embedding vector to characterize human voice, allowing for real-time isolation of main speaker audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional voice isolation methods are used, then speaker audio can be isolated, but computational resource requirements increase and user experience deteriorates
Solution Approach 1:
The system performs preliminary voice activity detection and speaker identification before full audio processing. By detecting voice activity and identifying speakers in advance, the system prepares embedding vectors and isolation parameters beforehand, reducing computational load during actual audio processing while maintaining high isolation quality
Solution Approach 2:
The system uses the audio data itself to generate speaker embedding vectors without requiring external enrollment processes. The speaker identification module extracts speaker characteristics directly from the audio stream, and the embedding generator creates vectors self-contained within the audio processing pipeline, eliminating the need for separate calibration or enrollment phases
2Measurement precision
If traditional enrollment processes are implemented, then accurate speaker models can be created, but the process becomes more complex and time-consuming
Solution Approach 1:
The system automatically generates speaker embedding vectors from the audio data itself without requiring users to participate in separate enrollment processes. The speaker identification module continuously analyzes audio streams and updates speaker models in real-time, making the system self-configuring and eliminating complex enrollment procedures while maintaining accurate speaker characterization
Solution Approach 2:
The system implements continuous feedback loops where speaker identification results from audio processing are fed back to update embedding vectors. This iterative process refines speaker models using actual usage data, improving accuracy over time without requiring manual enrollment or complex configuration processes
3Productivity
If real-time audio processing is performed, then voice call clarity improves, but computational resources are consumed
Solution Approach 1:
The system performs preliminary voice activity detection and speaker identification before full audio processing. By pre-processing audio data to identify active speakers and detect voice activity, the system prepares embedding vectors and isolation parameters in advance, enabling efficient real-time processing with reduced computational overhead during actual audio enhancement
Solution Approach 2:
The system applies audio processing selectively based on voice activity detection. When no voice activity is detected, full processing is skipped. When voice activity is present, processing is applied to relevant time segments only, reducing overall computational resource consumption while maintaining voice call clarity during active speech periods
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes: interface circuitry; instructions; and programmable circuitry to at least one of execute or instantiate the instructions to: calculate a sample embedding vector that characterizes a speaker based on a first audio signal; perform a first update of a personal embedding vector based on the sample embedding vector, the updated personal embedding vector to characterize the speaker based on a second audio signal and the first audio signal, and perform a second update of the personal embedding vector based on the first update and a universal embedding vector.