Hierarchical Speech Recognition Decoder with Parameterized Models
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Solution Overview
Problem
Speech recognition systems face challenges in expanding recognition capability while maintaining runtime efficiency and reducing the amount of model training data required, particularly in supporting context-specific data without increasing resource usage.
Innovation Solution
A computer-implemented method that generates user-specific interpretations by accessing a literal speech recognition corpus, applying a concept tagging module to identify concepts, and creating a parameterized statistical model with language sub-models customized for users, allowing for efficient interpretation of user utterances with placeholders for concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the language model supports recognition of context-specific data (e.g., specific names), then recognition capability is improved, but more data and retraining are required to build accurate models
Solution Approach 1:
The language model is segmented into a hierarchical structure with a generic language model handling common phrases and a set of specialized language sub-models handling context-specific concepts. Each sub-model focuses on a specific concept (e.g., person names, locations), allowing the system to expand recognition capability without requiring the entire model to be retrained with extensive context-specific data.
Solution Approach 2:
The generic language model serves as a universal base that handles general speech patterns and common expressions. Specialized language sub-models are added as needed for specific contexts, allowing the system to maintain a core universal model while adapting to multiple specific domains without requiring separate complete models for each context.
2Measurement precision
If the language model supports context-specific information, then recognition accuracy is improved, but CPU and memory resources increase
Solution Approach 1:
The system dynamically selects which language sub-models to activate based on the specific utterance being processed. Instead of loading all context-specific models into memory, the system only loads and activates the relevant sub-models needed for the current speech input, reducing CPU and memory usage while maintaining high recognition accuracy for context-specific terms.
Solution Approach 2:
Different parts of the language model have different levels of specialization. The generic language model handles common words with lower computational requirements, while specialized language sub-models provide high-precision recognition only for specific context-dependent concepts. This local quality approach ensures resources are concentrated where they are most needed for accuracy.
3Adaptability or versatility
If more data is acquired and retraining is performed to build accurate context-specific models, then recognition capability is improved, but runtime efficiency decreases
Solution Approach 1:
Language sub-models are pre-trained offline on context-specific data, so that during runtime, only the pre-trained models need to be loaded and applied, not trained from scratch. This preliminary action separates the data-intensive training phase from the efficiency-critical runtime phase, allowing extensive training data to be processed in advance without impacting runtime efficiency.
Data Source
AI summary
A speech interpretation module interprets the audio of user utterances as sequences of words. To do so, the speech interpretation module parameterizes a literal corpus of expressions by identifying portions of the expressions that correspond to known concepts, and generates a parameterized statistical model from the resulting parameterized corpus. When speech is received the speech interpretation module uses a hierarchical speech recognition decoder that uses both the parameterized statistical model and language sub-models that specify how to recognize a sequence of words. The separation of the language sub-models from the statistical model beneficially reduces the size of the literal corpus needed for training, reduces the size of the resulting model, provides more fine-grained interpretation of concepts, and improves computational efficiency by allowing run-time incorporation of the language sub-models.