# Evolutionary optimization of contexts for phonetic correction in speech recognition systems

arXiv: https://arxiv.org/abs/2102.11480 · HTML: https://mlsanity.com/paper/2102.11480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming](https://mlsanity.com/paper/1904.03368/) (2019)
- [Genetic Algorithm for a class of Knapsack Problems](https://mlsanity.com/paper/1903.03494/) (2019)
- [Sentiment analysis with genetically evolved Gaussian kernels](https://mlsanity.com/paper/1904.00977/) (2019)
- [A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems](https://mlsanity.com/paper/1904.04031/) (2019)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) is an area of growing academic and commercial interest due to the high demand for applications that use it to provide a natural communication method. It is common for general purpose ASR systems to fail in applications that use a domain-specific language. Various strategies have been used to reduce the error, such as providing a context that modifies the language model and post-processing correction methods. This article explores the use of an evolutionary process to generate an optimized context for a specific application domain, as well as different correction techniques based on phonetic distance metrics. The results show the viability of a genetic algorithm as a tool for context optimization, which, added to a post-processing correction based on phonetic representations, can reduce the errors on the recognized speech.