Voice recognition enhancement by genetic algorithm
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View abstract on PubMed
Summary
This summary is machine-generated.Genetic Algorithms (GA) enhance Artificial Neural Networks (ANN) for phoneme recognition, achieving over 95% accuracy. A hybrid SOM-GA model overcomes GA limitations for improved speech recognition performance.
Area Of Science
- Computational Linguistics
- Artificial Intelligence
- Machine Learning
Background
- Phoneme recognition is crucial for speech processing.
- Artificial Neural Networks (ANN) show promise but require effective optimization.
- Genetic Algorithms (GA) offer a robust optimization framework.
Purpose Of The Study
- To investigate the application of Genetic Algorithms (GA) for optimizing Artificial Neural Networks (ANN) in phoneme recognition.
- To evaluate the performance of GA in improving recognition rates compared to existing methods.
- To propose and validate a hybrid Self-Organizing Map (SOM) and GA model for enhanced phoneme recognition.
Main Methods
- Formalism, parameters, and operators of Genetic Algorithms (GA) were examined.
- A genetic strategy was developed for phoneme recognition using the TIMIT sound database.
- Experiments were conducted on TIMIT phonemes, comparing GA-optimized ANN with Self-Organizing Map (SOM) guided experiments.
- A hybrid SOM-GA model was proposed to leverage the strengths of both approaches.
Main Results
- Genetic Algorithms (GA) demonstrated improved phoneme recognition rates and global optimum search.
- Challenges with GA included premature convergence and overfitting, leading to unacceptable results.
- The hybrid SOM-GA model achieved an average recognition rate exceeding 95% on the TIMIT dataset.
- The hybrid approach effectively combined the properties of SOM and GA for superior performance.
Conclusions
- Genetic Algorithms (GA) are effective for optimizing Artificial Neural Networks (ANN) in phoneme recognition.
- A hybrid SOM-GA model significantly enhances phoneme recognition accuracy, surpassing individual methods.
- The proposed hybrid approach offers a promising solution for advanced speech recognition systems.
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