Dynamic Language Model for Speech Recognition Adaptation
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Solution Overview
Problem
Current language models in speech recognition systems, particularly Finite State Transducers (FSTs), are static and inflexible, failing to adapt to new applications and conditions, leading to suboptimal performance and requiring separate models for each application, which is operationally burdensome, especially when dealing with sparse data.
Innovation Solution
A combined language model is created using latent semantic analysis and pre-trained word embeddings to discover effective domains among applications, allowing for on-the-fly adaptation by interpolating weights based on runtime conditions such as applications, users, or device characteristics, thereby reducing the need for separate models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static language model like FST is used, then the model structure is simple and easy to manage, but the model cannot adapt to new applications and conditions, leading to suboptimal performance
Solution Approach 1:
The patent transforms the static FST language model into a dynamic system by introducing trainable weight parameters that can be adjusted based on runtime conditions. The model structure remains as an FST for simplicity, but incorporates adaptive weighting mechanisms that allow it to dynamically adapt to different applications, users, and device characteristics without requiring complete model redesign.
Solution Approach 2:
The patent modifies the traditional FST language model by introducing additional trainable weight parameters alongside the existing transition probabilities. These new parameters can be independently optimized for different applications and conditions, allowing the model to adapt its behavior by changing parameter values rather than restructuring the entire model architecture.
2Reliability
If separate language models are created for each application, then the performance for each application is optimized, but the operational burden increases significantly
Solution Approach 1:
The patent creates a universal language model that can serve multiple applications simultaneously. By introducing application-specific weight parameters that can be selectively activated or adjusted, a single model structure achieves the functionality of multiple separate models, reducing operational burden while maintaining application-optimized performance through parameter interpolation based on runtime conditions.
3Measurement precision
If a general N-gram model is used, then the model is simple to implement, but the perplexity is high and recognition accuracy is suboptimal
Solution Approach 1:
The patent creates a composite language model that combines the strengths of both FST and N-gram approaches. The model integrates FST's structured transition framework with N-gram's probabilistic language modeling capabilities, creating a hybrid system that achieves lower perplexity and higher recognition accuracy than either approach alone, while maintaining manageable complexity through shared parameter spaces and interpolation mechanisms.
Data Source
AI summary
A system to perform automatic speech recognition (ASR) using a dynamic language model. Portions of the language model can include a group of probabilities rather than a single probability. At runtime individual probabilities of the group are weighted and combined to create an adjusted probability for the portion of the language model. The adjusted probability can be used for ASR processing. The weights can be determined based on a characteristic of the utterance, for example an associated speechlet/application, the specific user speaking, or other characteristic. By applying the weights at runtime the system can use a single language model to dynamically adjust to different utterance conditions.