ASR Confidence Estimation via Frequency-Based Word Grouping
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Solution Overview
Problem
Automatic Speech Recognition (ASR) systems face challenges in accurately estimating the confidence of infrequently observed words, as their prior probabilities are unreliable due to 'noisy' probabilities resulting from a lack of observed examples, leading to correlated error rates that vary significantly with minor variations in correct predictions.
Innovation Solution
The system determines an estimated prior probability for infrequently observed words by grouping them with other words having similar observation frequencies, using a group prior probability until the number of observations exceeds a threshold, transitioning to an individual prior probability for more reliable confidence scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual prior probabilities are used for infrequently observed words, then the system can provide word-specific confidence scores, but the probabilities become unreliable due to noisy data from limited observations
Solution Approach 1:
The patent merges infrequently observed words into groups based on their observation frequencies. Instead of calculating individual prior probabilities for each infrequent word (which would be noisy), the system combines these words into groups and calculates a single group prior probability that reflects the aggregated observation data, thereby improving reliability while maintaining reasonable precision.
Solution Approach 2:
The patent introduces group prior probabilities as an intermediary between individual word probabilities and overall system confidence assessment. For infrequently observed words, the group prior probability acts as a mediator that provides a more stable and reliable confidence score than individual word probabilities could provide alone.
2Reliability
If the system waits for sufficient individual observations before using individual prior probabilities, then probability reliability improves, but the system must delay confidence scoring transitions
Solution Approach 1:
The patent performs preliminary grouping of infrequently observed words based on observation frequency thresholds before confidence scoring is needed. By pre-organizing words into groups and calculating group prior probabilities in advance, the system avoids delays during actual confidence assessment while ensuring reliable probability estimates are available immediately.
3Reliability
If group prior probabilities are used for all words, then probability reliability improves, but the system loses word-specific confidence differentiation
Solution Approach 1:
The patent applies different probability calculation strategies to different words based on their observation frequencies. Frequently observed words receive individual prior probabilities for precise differentiation, while infrequently observed words are grouped to ensure reliability. This local differentiation of quality maintains precision where possible while ensuring reliability where needed.
Data Source
AI summary
Devices, systems and methods are disclosed for estimating a prior probability for speech recognition by taking into account a number of observations of a particular word and a prior probability for a group of words having a similar number of observations. For example, a prior probability may be determined by combining a number of correct results and a number of observations for a group of words and calculating a prior probability of the entire group. Further, a prior probability may be determined for a word that was not previously observed by determining a prior probability for a group of words that have been observed once. The prior probability for a particular word may be determined differently as the number of observations increases and may transition from the group prior probability to an individual prior probability when the number of observations exceeds a threshold.