Converting Non-Back-Off Language Models for Speech Decoding
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face inefficiencies when using non-back-off language models due to their computationally expensive nature and the need for extensive n-gram calculations, which can lead to long decoding times and latency, especially when integrating neural network language models (NNLMs) directly into decoders.
Innovation Solution
Converting non-back-off language models into back-off language models using a background language model and representing them in the ARPA back-off format, followed by entropy-based pruning to reduce the number of n-grams and make the models more efficient for decoding without altering existing decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-back-off language models are used in ASR systems, then language modeling accuracy is improved, but decoding time and computational cost increase significantly
Solution Approach 1:
The patent converts the language model from non-back-off format to back-off format, changing the structural parameters of the model. This transformation allows the system to maintain high language modeling accuracy while reducing decoding time by using a more efficient model representation that avoids computationally expensive operations during speech decoding
Solution Approach 2:
The patent extracts and removes unnecessary n-grams from the language model through pruning operations. By identifying and eliminating low-probability or redundant n-grams, the system reduces the computational burden during decoding while preserving the most important language patterns needed for accurate speech recognition
2Measurement precision
If non-back-off language models are used in ASR systems, then language modeling accuracy is improved, but computational complexity increases
Solution Approach 1:
The conversion from non-back-off to back-off format changes the computational parameters of the language model. The back-off format uses a hierarchical structure with fewer parameters to store and process, reducing memory requirements and computational operations while maintaining modeling accuracy through the back-off mechanism that falls back to lower-order n-grams when higher-order ones are unavailable
Solution Approach 2:
The patent extracts and removes redundant n-grams through pruning, reducing the overall complexity of the language model. By eliminating unnecessary entries and simplifying the model structure, the system achieves lower computational complexity while retaining the essential language patterns needed for accurate speech recognition
3Measurement precision
If neural network language models are integrated directly into decoders, then language modeling capability is improved, but decoding latency increases
Solution Approach 1:
The patent transforms the neural network language model into back-off format, changing its structural parameters to be more suitable for real-time decoding. This conversion allows the model to maintain its language modeling capabilities while reducing latency by using a format that requires fewer computational operations during the decoding process
Solution Approach 2:
The patent extracts and removes unnecessary computations and model components through pruning, reducing the time required for decoding. By eliminating redundant n-grams and simplifying the model structure, the system achieves lower decoding latency while preserving the essential language modeling capabilities needed for accurate speech recognition
Data Source
AI summary
Techniques for conversion of non-back-off language models for use in speech decoders. For example, an apparatus for conversion of non-back-off language models for use in speech decoders. For example, an apparatus is configured convert a non-back-off language model to a back-off language model. The converted back-off language model is pruned. The converted back-off language model is usable for decoding speech.