Speech Segment Filtering for Lower Skip Rates in Data Labeling
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Solution Overview
Problem
The challenge of high skip rates during speech data labeling due to poor audio quality, noise, or low volume leads to wasted time and effort, necessitating an automatic method to detect segments likely to be skipped.
Innovation Solution
A method involving deep learning architectures, specifically CONFORMER and TRANSFORMER models with an Attentive Statistics Pooling Layer, to predict segment skipping likelihood, using a cross-entropy loss function and a threshold to filter data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human labelers manually listen to and transcribe all speech segments, then data quality can be maintained through human judgment, but productivity decreases due to high skip rates and wasted time on poor quality segments
Solution Approach 1:
The system performs preliminary quality assessment of speech segments using an automatic evaluation model before they reach human labelers. This preliminary action identifies and filters out poor quality segments that would likely be skipped, allowing labelers to focus only on suitable segments and thereby improving productivity while reducing time waste.
Solution Approach 2:
An automatic speech quality evaluation model serves as an intermediary between the raw speech data and human labelers. This intermediary assesses segment quality using multiple indicators (volume, noise, clarity) and provides recommendations, enabling the system to reduce skip rates by pre-filtering inappropriate segments before human review.
2Reliability
If all speech segments are presented to labelers for labeling, then data coverage is maximized, but resource efficiency decreases due to high skip rates on poor quality segments
Solution Approach 1:
The system implements a feedback mechanism where the automatic evaluation model continuously assesses speech segment quality and provides recommendations to the labeling system. This feedback loop allows the system to dynamically adjust which segments are presented to labelers, ensuring that only high-quality segments are labeled while maintaining overall data quality standards.
Solution Approach 2:
The system changes the parameter of segment selection by introducing multiple quality indicators (volume level, noise level, clarity score) and using these parameters to filter and prioritize segments. By evaluating segments based on these changed parameters rather than presenting all segments uniformly, the system improves both data quality and labeling efficiency.
3Productivity
If an automatic filtering system is implemented to reduce skip rates, then productivity improves, but system complexity increases due to the need for machine learning models and quality assessment mechanisms
Solution Approach 1:
The system employs a self-service approach where the automatic evaluation model independently assesses speech segment quality without requiring complex manual review processes. The model uses pre-trained indicators and algorithms to automatically determine segment suitability, reducing the need for complex human-in-the-loop validation mechanisms while maintaining high productivity.
Data Source
AI summary
The invention proposes a method to reduce the skip rate in speech data labeling, which is carried out through the following steps: Step 1: Collecting Speech Segments for Text Labeling; Step 2: Text Labeling of the Speech Segments; Step 3: Creating a Training Set for a Machine Learning Model; Step 4: Building the Machine Learning Model; Step 5: Training the Machine Learning Model; Step 6: Using the Machine Learning Model to Filter Data. The method helps reduce time and increase productivity in the speech data labeling process while ensuring data quality. The method employs a machine learning model to learn the behavior of skipping or not skipping speech segments by the labelers, thereby eliminating segments likely to be skipped before presenting the data to the labelers.