Utterance Grouping for Efficient Speech Model Label Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Labeling data for training machine learning models in speech-to-text systems is tedious and expensive, with later stage labeling often focusing on irrelevant regions, leading to inefficient model training and reduced accuracy.
Innovation Solution
Embedding audio samples into feature representations, grouping them based on confidence scores and embedding space coverage, and selectively labeling a subset of samples to improve training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If later stage labeling focuses on all available data samples, then the model training process is completed, but the labeling effort is wasted on samples where the model is already accurate and on irrelevant sparse regions
Solution Approach 1:
The patent segments the data space by dividing all audio samples into multiple clusters based on their embedding representations. This segmentation allows the system to identify and focus on specific clusters that contain ambiguous or uncertain samples, rather than uniformly processing all samples. By segmenting the data space into meaningful groups, the system can selectively apply labeling resources to only those segments that benefit from additional training data.
Solution Approach 2:
The patent applies local quality by assigning different labeling priorities to different regions of the data space. Instead of uniform labeling across all samples, the system identifies regions with high uncertainty or ambiguity (where model predictions are less confident) and focuses labeling efforts on those specific local regions. This ensures that labeling resources are concentrated on areas where they will have the most impact on improving model accuracy.
2Adaptability or versatility
If later stage labeling focuses on sparse regions with few audio streams, then those regions are covered, but the overall model improvement is minimal since these regions are irrelevant
Solution Approach 1:
The patent segments the data space into multiple clusters and evaluates each cluster's relevance to overall model improvement. By segmenting the data, the system can identify and prioritize clusters that contain dense, relevant audio samples over sparse, irrelevant regions. This segmentation enables selective focus on high-value regions while minimizing investment in low-value sparse regions.
Solution Approach 2:
The patent changes the selection criteria for labeling by using embedding space density as a key parameter. Instead of selecting samples based solely on coverage or uniform distribution, the system uses density metrics to identify regions with concentrated, relevant samples. This parameter change shifts the focus from covering all regions to prioritizing high-density, high-value regions that contribute more to model improvement.
3Extent of automation
If the entire sequence is represented for active learning, then active learning can be performed, but it is difficult to ensure each discrete frame has its own appropriate label
Solution Approach 1:
The patent segments the sequence data into discrete clusters based on embedding representations. Each cluster represents a group of similar samples that share common characteristics. By segmenting the sequence into clusters rather than treating it as a continuous whole, the system can assign labels at the cluster level while ensuring that each discrete frame within a cluster receives appropriate labeling based on its specific characteristics.
Solution Approach 2:
The patent introduces embedding representations as an intermediary between the raw audio samples and the labeling process. These embeddings serve as a bridge that captures the essential features of each sample while enabling efficient clustering and selection. The embedding space acts as an intermediary representation that facilitates both automated sample selection and precise label assignment, resolving the conflict between automation and precision.
Data Source
AI summary
A system and method for training a machine learning model using sample labeling. A method includes embedding a first plurality of audio samples into a plurality of embeddings, wherein each embedding is a feature representation of a respective portion of the first plurality of audio samples; grouping the plurality of embeddings into a plurality of groups; selecting a second plurality of audio samples to be labeled based on the plurality of groups, wherein the second plurality of audio samples is selected based on a plurality of confidence scores for the plurality of groups and based on an embedding space coverage for each of the plurality of groups; and labeling the selected second plurality of audio samples by applying an automated speech recognition model to the second plurality of audio samples.