How does the hidden Markov model enhance the effectiveness of voice recognition systems?

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1. The hidden Markov model (HMM) is used in voice recognition to analyze speech by decomposing spoken words into smaller phonetic units called phonemes.

2. This granular breakdown allows the system to better capture the unique sound patterns and variations in speech.

3. By focusing on phonemes, the HMM facilitates a more precise and detailed understanding of the spoken input, which improves the accuracy and efficiency of voice recognition.

4. This contrasts with other models like recurrent neural networks, which use previous outputs to influence current inputs.

**Answer:** It breaks down words into phonetic units, allowing a more detailed analysis in voice recognition.

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