Optimized Language Model Interpolation for Speech Recognition
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Solution Overview
Problem
Implementing robust and effective speech recognition systems in electronic devices is challenging due to increased processing and hardware demands, which result in higher production costs and operational inefficiencies, especially for systems requiring advanced functionalities.
Innovation Solution
A method that iteratively generates and optimizes language models by interpolating source models using adjustable coefficients, where the lambda value is incremented to minimize word-error rates, allowing for the selection of an optimized language model for improved speech recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enhanced system functionality and performance are implemented, then speech recognition capability is improved, but processing power requirements and hardware resources increase
Solution Approach 1:
The language model is segmented into multiple source models (e.g., trigram, bigram, unigram models) that can be independently processed and combined. This segmentation allows the system to distribute processing requirements across multiple smaller models rather than requiring one large complex model, thereby improving speech recognition capability while managing processing power requirements more efficiently
Solution Approach 2:
Multiple source models are merged through linear interpolation to create a unified language model. The formula LM = λ1SM1 + λ2SM2 + ... + λnSMn combines several simpler source models into a more powerful language model, achieving enhanced speech recognition capability without requiring a single large complex model that would demand excessive processing power
2Reliability
If enhanced system functionality and performance are implemented, then speech recognition capability is improved, but production costs increase
Solution Approach 1:
The system changes parameters by adjusting the lambda coefficients (λ1, λ2, ..., λn) in the linear interpolation formula to optimize language model performance. This parameter optimization allows the system to achieve high speech recognition capability using standard hardware resources, avoiding the need for expensive specialized hardware and thereby reducing production costs
3Reliability
If enhanced system functionality and performance are implemented, then speech recognition capability is improved, but operational efficiency decreases
Solution Approach 1:
The language model is pre-compiled and optimized before deployment, with lambda coefficients determined in advance through offline optimization processes. This preliminary action allows the system to achieve high speech recognition capability during operation without incurring high computational costs in real-time processing, thereby maintaining operational efficiency
Data Source
AI summary
A system and method for effectively implementing an optimized language model for speech recognition includes initial language models each created by combining source models according to selectable interpolation coefficients that define proportional relationships for combining the source models. A rescoring module iteratively utilizes the initial language models to process input development data for calculating word-error rates that each correspond to a different one of the initial language models. An optimized language model is then selected from the initial language models by identifying an optimal word-error rate from among the foregoing word-error rates. The speech recognizer may then utilize the optimized language model for effectively performing various speech recognition procedures.