Event-Based Speech Recognition Using Adaptive Sampling
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Solution Overview
Problem
Current automatic speech recognition systems fail to effectively exploit temporal information and variability in speech signals, leading to inaccurate recognition due to uniform time step sampling and frame-based methods that do not synchronize with speech events, resulting in increased variability and loss of temporal details.
Innovation Solution
The development of event-based detectors and classifiers that focus on the most robust and relevant aspects of the speech signal, using adaptive boosting algorithms and weighted classifiers to extract and synchronize with speech events, allowing for the capture of temporal relationships and synchronization with speech events, thereby improving recognition accuracy and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform time step sampling is used, then the system is simple to implement, but temporal details are quantized and variability increases
Solution Approach 1:
The patent changes the sampling parameter from fixed uniform intervals to variable intervals synchronized with speech events. The system detects speech events (onsets, offsets, stress points) and samples the signal at these dynamically determined time points, allowing the sampling rate to adapt to the temporal structure of speech rather than using a constant sampling rate.
Solution Approach 2:
The system transitions from static uniform sampling to dynamic event-synchronized sampling. The sampling process becomes adaptive, with the timing of samples determined by detected speech events rather than predetermined fixed intervals. This allows the system to concentrate sampling resources when speech activity occurs and reduce sampling during silence periods.
2Ease of manufacture
If frame-based methods with uniform analysis frames are used, then processing is straightforward, but the relationship with speech events becomes random and temporal variability increases
Solution Approach 1:
The system performs preliminary detection of speech events (onsets, offsets, stressed syllables) before the main recognition processing. These detected events are stored and used to guide subsequent analysis, ensuring that feature extraction and classification are synchronized with actual speech events rather than arbitrary frame boundaries.
Solution Approach 2:
The patent introduces an intermediary event detection layer between the raw signal and the recognition system. This intermediary component detects and marks speech events, which then serve as reference points for synchronizing the analysis of subsequent signal portions. This mediator ensures that the recognition system processes information at meaningful temporal landmarks.
3Ease of operation
If all frames are treated as equally important, then processing is uniform, but information with better signal-to-noise ratio and higher relevance is not prioritized
Solution Approach 1:
The system applies different processing qualities to different portions of the signal based on their importance. Frames containing detected speech events (onsets, offsets, stressed syllables) are processed with higher priority and more detailed analysis, while frames between events receive less intensive processing. This local differentiation of processing quality matches the non-uniform importance of different speech regions.
Solution Approach 2:
The system applies excessive (more intensive) processing to critical speech event frames and partial (reduced) processing to less important frames. Rather than uniformly processing all frames at the same level, the system concentrates computational resources on frames that contain the most diagnostically valuable information for speech recognition.
4Reliability
If Hidden Markov Models with self-transitioning states are used, then temporal variability is absorbed, but durational information and temporal relationships are removed
Solution Approach 1:
The system extracts durational information and temporal relationships as separate explicit features from the HMM processing. Rather than relying solely on the HMM's probabilistic state transitions, the system measures and records actual durations between speech events and temporal patterns, then uses these extracted features in addition to or instead of HMM outputs to preserve timing information.
Data Source
AI summary
Speech recognition is performed in near-real-time and improved by exploiting events and event sequences, employing machine learning techniques including boosted classifiers, ensembles, detectors and cascades and using perceptual clusters. Speech recognition is also improved using tandem processing. An automatic punctuator injects punctuation into recognized text streams.