Histogram Pre-Pruning for Active HMM States
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Solution Overview
Problem
Speech recognition systems place a significant load on computing resources, affecting processing speed and memory usage, especially in real-time applications and handheld devices with limited resources.
Innovation Solution
A speech processing device with a processing unit, histogram pruning unit, and pre-pruning unit is used to calculate Hidden Markov Model (HMM) pruning thresholds, prune HMM states, and pre-prune active states based on adjustable thresholds, reducing computational and memory-intensive tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems process all HMM states completely, then recognition accuracy is improved, but computing resource load and processing time increase significantly
Solution Approach 1:
The system performs preliminary pruning of HMM states before complete processing by calculating pruning thresholds and removing low-probability states in advance. This pre-processing step reduces the number of states that require full evaluation, thereby decreasing computing resource load while maintaining recognition accuracy for the remaining high-probability states.
Solution Approach 2:
Instead of processing all HMM states completely, the system applies partial processing by selectively evaluating only the most probable states after threshold-based pruning. This partial action approach reduces computational effort while sufficient accuracy is maintained for the subset of states that pass the pruning criteria.
2Measurement precision
If speech recognition systems process all HMM states completely, then recognition accuracy is improved, but memory resource consumption increases
Solution Approach 1:
The system extracts and removes low-probability HMM states from the complete state space through threshold-based pruning. By taking out the least probable states, the system reduces the memory footprint required to store and process HMM states while maintaining the essential information needed for accurate recognition in the reduced state set.
Solution Approach 2:
Memory optimization is achieved through preliminary identification and removal of unnecessary states before full processing. The pruning thresholds are calculated in advance to identify which states can be discarded, reducing the active memory requirements for HMM state storage and processing.
3Productivity
If speech recognition systems use fixed pruning thresholds, then processing speed is improved, but adaptability to different acoustic conditions decreases
Solution Approach 1:
The system transitions from fixed to dynamic pruning thresholds that automatically adjust based on acoustic conditions. The pruning threshold is calculated as a function of the maximum phoneme score and acoustic conditions, allowing the threshold to vary adaptively. This dynamic adjustment maintains processing speed benefits while improving adaptability to different speaking conditions, noise levels, and acoustic environments.
Solution Approach 2:
The pruning threshold parameter is made variable rather than fixed, changing based on acoustic condition metrics and phoneme scores. This parameter change allows the system to optimize the balance between processing speed and adaptability by adjusting the threshold according to the specific acoustic context of each speech recognition task.
Data Source
AI summary
Embodiments of the present invention include an acoustic processing device, a method for acoustic signal processing, and a speech recognition system. The speech processing device can include a processing unit, a histogram pruning unit, and a pre-pruning unit. The processing unit is configured to calculate one or more Hidden Markov Model (HMM) pruning thresholds. The histogram pruning unit is configured to prune one or more HMM states to generate one or more active HMM states. The pruning is based on the one or more pruning thresholds. The pre-pruning unit is configured to prune the one or more active HMM states based on an adjustable pre-pruning threshold. Further, the adjustable pre-pruning threshold is based on the one or more pruning thresholds.