Transformer-Based WER Estimation for Automatic Speech Recognition
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges in accurately estimating word error rates (WER) due to the labor-intensive nature of manual labeling and the unsuitability of known statistical distributions for modeling empirical WER distributions, particularly in scenarios where most words are recognized correctly with errors being a minority.
Innovation Solution
A regression model is trained to predict WERs using a transformer learning model, combining a memory encoder with a masking strategy and a hybrid statistical distribution comprising a continuous beta distribution and a discrete Bernoulli distribution to model both perfect and imperfect performance, overcoming dual-modality inputs and skewed error frequency distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to determine error rates, then measurement precision of WER is improved, but productivity deteriorates due to high labor intensity
Solution Approach 1:
The system uses the ASR model's own output probabilities and confidence scores to self-evaluate its error rates without external manual labeling. The QE model leverages the ASR model's internal signals (log probabilities, confidence scores) to automatically determine WER, making the system self-sufficient and eliminating labor-intensive manual verification.
Solution Approach 2:
A quality estimation (QE) model is introduced as an intermediary between the ASR model and the evaluation process. This QE model acts as a mediator that predicts WER based on ASR outputs and auxiliary features, replacing the need for manual labeling while maintaining measurement precision through learned patterns from training data.
2Productivity
If known statistical distributions are used to model error rates, then productivity is improved by avoiding manual labeling, but measurement precision deteriorates because known distributions are ill-suited for ASR WER distributions
Solution Approach 1:
The system transforms the WER distribution modeling problem by changing the parameterization approach. Instead of using fixed known distributions, the QE model learns distribution parameters (mean, variance) dynamically from training data, adapting to the specific characteristics of ASR error distributions which are typically skewed with most words recognized correctly.
Solution Approach 2:
The solution combines multiple modeling components into a composite QE model that integrates ASR output probabilities, confidence scores, and learned statistical patterns. This composite approach creates a customized distribution model that better captures the empirical WER characteristics than any single known statistical distribution could alone.
3Measurement precision
If regression modeling is applied to ASR error rates, then measurement precision is improved, but it fails because no known statistical distributions can be readily applied to skewed error distributions where majority of words are recognized correctly
Solution Approach 1:
The QE model employs dynamic parameter adjustment where the statistical distribution parameters (mean, variance) are not fixed but are learned and adapted during training. The model dynamically adjusts its predictions based on input features and learned patterns, making it adaptable to different ASR systems and error distribution characteristics rather than relying on static known distributions.
Solution Approach 2:
The system changes the approach from using fixed distribution parameters to learning parameters from data. The QE model trains on labeled data to learn the relationship between ASR outputs and actual WER, capturing the skewed distribution characteristics where most words are correct. This parameter learning enables accurate regression modeling adapted to ASR-specific error patterns.
Data Source
AI summary
Methods and systems are provided for implementing quality estimation for automatic speech recognition, and more specifically training an ASR model, and training a QE model to perform word error rate prediction upon the trained ASR model. The ASR model may be a transformer learning model having an architecture including an encoder including multi-head attention layers, and a memory encoder including a masking multi-head attention layer. The QE model may include a binary classification model and a regression model, where the binary classification model is based on a discrete statistical distribution, and the regression model is based on a continuous statistical distribution. Training the ASR model may produce output having variable word error rates, and the QE model may be trained based on empirical word error rates of the ASR model. The QE model may predict performance of the ASR model without labor-intensive labeling to generate ground truth.