Recurrent Audio Enhancement Models for Enrollment-Free Speech Personalization
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Solution Overview
Problem
Conventional machine learning techniques for processing time-varying signals, such as audio signals, are cumbersome due to the need for separate speaker embedding models and enrollment steps, which complicate the personalization process.
Innovation Solution
A unified approach that generates speaker embeddings within an audio enhancement model, eliminating the need for a separate embedding model by using the model's recurrent component to produce embeddings on the fly and continuously update them during user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate speaker embedding models and enrollment steps are used for audio signal processing, then speaker identification accuracy is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent merges the speaker embedding model with the audio enhancement model into a single unified model. The enhancement model includes a speaker embedding generation component that produces speaker embeddings internally during the audio enhancement process, eliminating the need for separate embedding models and enrollment steps. This integration maintains speaker identification accuracy while reducing overall system complexity.
Solution Approach 2:
The audio enhancement model is designed to perform multiple functions: it enhances audio signals while simultaneously generating speaker embeddings for identification. The model takes audio signals as input and produces both enhanced audio output and speaker embedding representations, making the single model universal for both enhancement and speaker identification tasks.
2Measurement precision
If separate speaker embedding models are used, then speaker characteristics can be captured accurately, but processing time and computational resources increase
Solution Approach 1:
By combining the speaker embedding generation functionality within the audio enhancement model, the patent eliminates the need for separate processing stages. The model generates speaker embeddings concurrently with audio enhancement in a single forward pass, significantly reducing processing time and computational overhead compared to separate models.
Solution Approach 2:
The speaker embedding generation occurs continuously during the audio enhancement process rather than requiring separate enrollment steps. The model continuously updates and refines speaker embeddings as it processes audio signals, maintaining accurate speaker characteristics capture while ensuring continuous processing without interruptions.
3Adaptability or versatility
If enrollment steps are required for speaker embedding generation, then speaker-specific customization is improved, but ease of operation decreases
Solution Approach 1:
The audio enhancement model performs self-service by automatically generating speaker embeddings during the normal audio enhancement process. No separate enrollment steps are required from the user perspective - the model autonomously captures speaker characteristics as it processes audio signals, making the system easier to operate while maintaining speaker-specific customization.
Solution Approach 2:
The model performs preliminary speaker embedding generation during the initial audio enhancement processing. Speaker characteristics are captured and stored in advance within the model's internal representations, enabling personalized audio enhancement for each speaker without requiring explicit enrollment procedures or user configuration.
Data Source
AI summary
This document relates to enhancement of time-varying signals, such as audio signals. For instance, some implementations can compute a representation of the characteristics of a user's speech within a trained enhancement model. The representation can be employed to personalize the enhancement model, e.g., by suppressing sounds from sources other than the user's speech. In some cases, the representation can be computed based on a hidden state of a recurrent layer of the trained enhancement model.