Dynamic Language Model Adaptation for ASR Latency
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Solution Overview
Problem
Speech processing systems face computational resource challenges due to the large size of language models, which makes it difficult to deploy complex models and efficiently update them during runtime, leading to latency and reduced accuracy in automatic speech recognition (ASR) and natural language understanding (NLU) processes.
Innovation Solution
A system that dynamically adapts language models during runtime by focusing on relevant domains through partial ASR hypotheses processing, using a trained classifier to determine domain-specific models and update language model weights, allowing for efficient resource allocation and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to improve speech recognition accuracy, then ASR and NLU accuracy is improved, but computational resource requirements increase and latency increases
Solution Approach 1:
The language model is segmented into multiple domain-specific models (e.g., weather domain, navigation domain, music domain). Instead of using one large monolithic model, the system divides the language model into smaller, specialized models that can be selectively activated based on the speech input domain, thereby reducing overall computational resource requirements while maintaining high accuracy for each specific domain.
Solution Approach 2:
The system dynamically selects and switches between different domain-specific language models based on real-time analysis of the speech input. The classifier continuously monitors the domain of the incoming speech and adapts the active language model accordingly, enabling the system to optimize computational resources by only loading and processing with the relevant domain model at any given time, thus reducing latency and energy consumption.
2Measurement precision
If large language models are used to improve speech recognition accuracy, then ASR and NLU accuracy is improved, but system complexity increases
Solution Approach 1:
The complex language model is segmented into multiple smaller domain-specific models, each handling a particular domain (weather, navigation, music, etc.). This segmentation reduces the complexity of any single model that needs to be loaded and processed, making the overall system more manageable and easier to optimize for each specific domain while maintaining high accuracy.
Solution Approach 2:
A domain classifier is introduced as an intermediary component between the speech input and the language model selection process. The classifier analyzes the domain of the incoming speech input and mediates the selection of the appropriate language model, thereby simplifying the system architecture by providing a clear decision-making framework and reducing the complexity of model management.
3Adaptability or versatility
If language models are updated during runtime to adapt to domains, then adaptability is improved, but computational overhead increases
Solution Approach 1:
Multiple domain-specific language models are pre-trained and prepared in advance before runtime processing. The system loads these pre-computed models into memory ready for immediate use. When domain adaptation is needed, the system simply switches between the pre-prepared models rather than performing computationally intensive training during runtime, thereby achieving high adaptability while minimizing computational overhead during actual speech processing.
Solution Approach 2:
The system dynamically switches between different domain-specific language models based on the detected speech domain. This dynamic model selection allows the system to adapt to different domains in real-time without performing computationally expensive training operations during runtime. The classifier continuously monitors the domain and adapts the active model accordingly, achieving versatility with minimal computational overhead.
4Productivity
If domain-specific language models are selected based on partial ASR hypotheses, then processing efficiency is improved, but measurement precision requirements increase
Solution Approach 1:
The domain classification is performed preliminarily on partial ASR hypotheses before the complete speech recognition process begins. By analyzing the initial hypotheses and determining the domain early in the processing pipeline, the system can select the appropriate language model in advance, thereby improving processing efficiency. The preliminary domain detection allows for optimized resource allocation and faster subsequent processing.
Solution Approach 2:
The system dynamically adjusts the language model selection based on the domain detected in the partial ASR hypotheses. This dynamic approach allows the system to optimize processing efficiency by matching the speech input domain with the most appropriate pre-trained language model. The continuous monitoring and adaptation of the active model based on domain detection enables efficient resource utilization while maintaining high processing throughput.
Data Source
AI summary
Exemplary embodiments relate to adapting a generic language model during runtime using domain-specific language model data. The system performs an audio frame-level analysis, to determine if the utterance corresponds to a particular domain and whether the ASR hypothesis needs to be rescored. The system processes, using a trained classifier, the ASR hypothesis (a partial hypothesis) generated for the audio data processed so far. The system determines whether to rescore the hypothesis after every few audio frames (representing a word in the utterance) are processed by the speech recognition system.