Speech Recognition Accuracy Prediction via Feature Extraction
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Solution Overview
Problem
Current automatic speech recognition systems are inefficient in predicting recognition accuracy without processing large audio files, making it time-consuming and impractical for real-world deployment, especially in applications requiring high detection rates and low false alarms across multiple languages and channel conditions.
Innovation Solution
A method using machine learning to predict speech recognition performance by computing a Feature of Merit (FOM) score based on relevant features such as phonemes, syllables, and pronunciation, allowing for the creation of a mapping that can generalize to new keywords, enabling accurate prediction without extensive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition accuracy is measured by processing large audio files, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary analysis by extracting relevant features (phoneme counts, syllable structures, pronunciation patterns) from keywords before actual speech recognition deployment. This preliminary feature extraction creates a foundation for accurate prediction without requiring full audio processing, thus saving time while maintaining measurement precision.
Solution Approach 2:
The invention extracts only the essential features needed for accuracy prediction (phoneme characteristics, syllable patterns, pronunciation features) from the complete audio processing pipeline. By taking out just these critical elements and using them to train a prediction model, the system achieves accurate recognition predictions without processing entire audio files, thereby reducing time loss.
2Reliability
If speech recognition performance is evaluated through full audio file processing, then reliability is improved, but productivity and resource consumption worsen
Solution Approach 1:
Instead of processing actual audio files to evaluate speech recognition performance, the system creates a predictive model that copies the essential recognition patterns from training data. This model then predicts performance on new keywords without requiring actual audio processing, maintaining reliability through pattern matching while dramatically improving productivity by avoiding repetitive full-file processing.
Solution Approach 2:
The invention changes the evaluation parameters from processing complete audio files to analyzing extracted linguistic features (phoneme counts, syllable structures, pronunciation patterns). By transforming the input parameters from raw audio data to processed linguistic characteristics, the system achieves reliable performance evaluation with significantly improved processing efficiency.
3Measurement precision
If recognition threshold filtering is applied to separate target words, then measurement precision is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The system performs preliminary feature extraction and training before deployment, creating a pre-computed prediction model that encodes the threshold filtering logic. During actual operation, the pre-trained model directly predicts recognition accuracy without requiring complex real-time threshold filtering operations, thus maintaining measurement precision while reducing system complexity during execution.
Data Source
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AI summary
A system and method are presented for predicting speech recognition performance using accuracy scores in speech recognition systems within the speech analytics field. A keyword set is selected. Figure of Merit (FOM) is computed for the keyword set. Relevant features that describe the word individually and in relation to other words in the language are computed. A mapping from these features to FOM is learned. This mapping can be generalized via a suitable machine learning algorithm and be used to predict FOM for a new keyword. In at least embodiment, the predicted FOM may be used to adjust internals of speech recognition engine to achieve a consistent behavior for all inputs for various settings of confidence values.