Speech Encoder Training with Cluster-Weighted Discrete Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech encoding models trained using unsupervised pre-training with discrete labels suffer from weak anti-noise capability and are prone to overfitting, leading to instability and reduced effectiveness in speech recognition tasks.
Innovation Solution
A method for training a speech encoding model that involves generating discrete features, clustering centers, and adjusting model parameters based on training loss derived from distances to clustering centers, using a first and second speech encoding model to improve model stability and capability with reduced data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unsupervised pre-training with discrete labels is used, then training data requirements are reduced, but model stability and anti-noise capability deteriorate
Solution Approach 1:
The training process is segmented into two distinct stages: unsupervised pre-training with discrete labels to reduce data requirements, followed by supervised fine-tuning with continuous labels to improve stability and anti-noise capability. This segmentation allows each stage to optimize for its specific goal without compromising the other.
Solution Approach 2:
The unsupervised pre-training stage serves as a preliminary action that initializes the model with basic speech representation capabilities using minimal labeled data. This preliminary training prepares the model for subsequent supervised fine-tuning, where the model's stability and anti-noise capability are enhanced through continuous label guidance.
2Productivity
If unsupervised pre-training with discrete labels is used, then training efficiency is improved, but model effectiveness in speech recognition tasks deteriorates
Solution Approach 1:
The training process is divided into two phases: an unsupervised pre-training phase that achieves rapid initialization with high efficiency, and a supervised fine-tuning phase that optimizes model effectiveness for speech recognition tasks. Each phase contributes differently to the overall training outcome.
Solution Approach 2:
The supervised fine-tuning stage acts as an intermediary that bridges the gap between the efficient but less effective unsupervised pre-training and the final speech recognition performance. This intermediate step transfers and refines the learned representations for task-specific effectiveness.
3Quantity of substance
If traditional unsupervised pre-training methods are used, then data usage is reduced, but model capability and stability deteriorate
Solution Approach 1:
Traditional unsupervised pre-training methods perform the preliminary action of initializing model parameters with minimal data, establishing basic representation capabilities. The proposed two-stage approach builds on this by adding supervised fine-tuning as a subsequent action that enhances capability and stability without significantly increasing data usage.
Solution Approach 2:
The model transitions between different parameter regimes: during unsupervised pre-training, parameters are optimized for general speech representation with discrete labels; during supervised fine-tuning, parameters are adjusted to maximize speech recognition performance with continuous labels, thereby improving capability and stability.
Data Source
AI summary
Embodiments of the disclosure relate to a method, an apparatus, a device and a readable medium for training a speech encoding model. An example method includes: processing, by using a first speech encoding model, a speech feature representation of a speech sample to generate a set of discrete features; generating label information based on the set of discrete features, the label information comprising a set of labels indicating a clustering center corresponding to a respective discrete feature; processing, by using a second speech encoding model, an intermediate feature representation generated based on the speech feature representation to generate probability information corresponding to the label information; determining a training loss based on the label information, the probability information, and weight information determined based on a distance from a respective discrete feature to a corresponding clustering center; and adjusting a parameter of the second speech encoding model based on the training loss.