Context-Dependent Speech Recognizer Non-Uniform State Clustering
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Solution Overview
Problem
Context-dependent speech recognizers are less robust to environmental changes compared to context-independent models, requiring more memory and computational resources and struggling with accuracy in mismatched conditions.
Innovation Solution
An enhanced multiple state context-dependent Hidden Markov Model (HMM) is developed with reduced observation distributions for states less affected by contextual dependencies, using non-uniform state clustering to tie observation distributions and define some states as context-independent, thereby reducing the number of possible distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context-dependent HMM is used to improve speech recognition accuracy, then recognition accuracy in matched conditions is improved, but robustness to environmental changes deteriorates
Solution Approach 1:
The HMM states are segmented into two categories: context-dependent states (initial and body states) that maintain full observation distributions for accuracy, and context-independent states (final states) that use tied observation distributions for robustness. This segmentation allows the system to selectively apply different modeling strategies to different parts of the speech recognition process.
Solution Approach 2:
Different quality levels of observation distributions are applied locally to different states. Final states use tied (lower quality) observation distributions that are robust to environmental changes, while initial and body states use full (higher quality) context-dependent observation distributions. This local differentiation resolves the contradiction by optimizing for the right property in the right place.
2Measurement precision
If context-dependent HMM with full observation distributions is used, then speech recognition accuracy is improved, but memory requirements increase
Solution Approach 1:
The observation distributions are segmented such that only the necessary states (initial and body states) maintain full context-dependent distributions, while final states use tied distributions. This reduces the total quantity of observation distribution parameters stored in memory while preserving accuracy where it matters most.
Solution Approach 2:
Full-quality observation distributions are maintained locally only where context dependency is most beneficial (initial and body states), while final states use reduced-quality tied distributions. This local optimization reduces overall memory footprint while maintaining recognition accuracy.
3Measurement precision
If context-dependent HMM with full observation distributions is used, then speech recognition accuracy is improved, but computational resources increase
Solution Approach 1:
Computational resources are segmented by state type: context-dependent computation is performed only for initial and body states where it provides value, while final states use efficient tied distribution evaluation. This reduces overall CPU usage while maintaining accuracy.
Solution Approach 2:
High-quality context-dependent computation is applied locally only where needed (initial and body states), while final states use lower-computation tied distributions. This local differentiation reduces total computational energy consumption while preserving recognition accuracy.
4Stability of the object's composition
If uniform state clustering is applied to all states, then model consistency is maintained, but robustness in states less affected by context is reduced
Solution Approach 1:
The clustering approach is segmented by state type: uniform clustering is applied to initial and body states to maintain consistency, while final states use non-uniform clustering with higher tie thresholds to improve robustness. This segmentation allows different clustering strategies optimized for each state type.
Solution Approach 2:
Different clustering quality levels are applied locally: tight clustering maintains consistency where context matters (initial and body states), while loose clustering with higher tying improves robustness in final states where context has less impact. This resolves the contradiction by optimizing clustering for each state type's specific needs.
Data Source
AI summary
An apparatus to improve robustness to environmental changes of a context dependent speech recognizer for an application, that includes a training database to store sounds for speech recognition training, a dictionary to store words supported by the speech recognizer, and a speech recognizer training module to train a set of one or more multiple state Hidden Markov Models (HMMs) with use of the training database and the dictionary. The speech recognizer training module performs a non-uniform state clustering process on each of the states of each HMM, which includes using a different non-uniform cluster threshold for at least some of the states of each HMM to more heavily cluster and correspondingly reduce a number of observation distributions for those of the states of each HMM that are less empirically affected by one or more contextual dependencies.