Attention-Based Biasing for Key Phrase Spotting
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Solution Overview
Problem
Existing automatic speech recognition systems are inefficient in detecting user-defined key phrases without retraining the system, as they are limited to recognizing a fixed set of phrases and require significant computational resources for broader language recognition.
Innovation Solution
The implementation of an attention-based biasing mechanism within a key phrase spotting system, utilizing a key phrase encoder network, attention layers, and a prediction network to detect user-defined key phrases by analyzing audio signals and predicting sub-word units, allowing for the detection of different key phrases without retraining the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a speech recognition system is trained to recognize a fixed set of phrases, then it can accurately recognize those phrases, but it cannot detect user-defined key phrases without retraining
Solution Approach 1:
The system dynamically adapts its detection focus through an attention mechanism that can be reconfigured without retraining. The attention weights are adjusted based on the target key phrase, allowing the same neural network to detect different user-defined phrases by simply changing the attention biasing parameters rather than retraining the entire model.
Solution Approach 2:
The invention changes the parameters of the attention mechanism to adapt to different key phrases. By modifying attention weights and biasing parameters rather than retraining the neural network, the system can detect arbitrary user-defined key phrases while maintaining computational efficiency and detection accuracy.
2Adaptability or versatility
If a speech recognition system analyzes audio signals to recognize a larger set of words, then it provides broader language recognition, but it consumes significant computational resources
Solution Approach 1:
The system extracts only the necessary computational focus from the entire audio signal by using attention mechanisms to identify and amplify only the relevant portions corresponding to the target key phrase. This allows the system to process audio signals efficiently without needing to analyze all possible words and phrases, thereby reducing computational resource consumption while maintaining language recognition capability.
Solution Approach 2:
Instead of processing the entire audio signal for all possible words (excessive action), the attention-based system performs partial analysis by focusing computational resources only on the specific key phrase patterns of interest, achieving sufficient detection accuracy without the full computational burden of comprehensive speech recognition.
3Productivity
If a key phrase spotting system continuously analyzes audio signals to detect key phrases, then it provides real-time detection, but it increases false rejection rates without attention-based biasing
Solution Approach 1:
The attention-based biasing mechanism provides feedback about the contextual importance of different audio segments to the detection process. By continuously monitoring and adjusting attention weights based on the audio context, the system can distinguish between actual key phrase occurrences and false patterns, thereby reducing false rejection rates while maintaining real-time detection capability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers; generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.