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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Published on: July 22, 2025

Random forests, sound symbolism and Pokémon evolution.

Alexander James Kilpatrick¹, Aleksandra Ćwiek², Shigeto Kawahara³

¹International Communication, Nagoya University of Commerce and Business, Nagoya, Aichi, Japan.

|January 4, 2023

Summary

This summary is machine-generated.

Machine learning algorithms effectively classify Pokémon evolution using sound symbolism, outperforming human participants in recognizing sound-meaning patterns. This study highlights AI

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Area of Science:

Computational Linguistics
Artificial Intelligence
Cognitive Science

Background:

Sound symbolism, the idea that sounds in words correlate with meaning, is a recognized linguistic phenomenon.
Machine learning models can potentially learn and apply these sound-meaning correspondences.
Pokémon names offer a unique dataset for studying sound symbolism due to their varied origins and evolutionary transformations.

Purpose of the Study:

To construct and train machine learning algorithms (random forests) to classify Pokémon based on sound symbolism.
To compare the classification accuracy of these algorithms against human participants.
To investigate the role of name length as a feature in sound-meaning classification.

Main Methods:

Training random forests on names of Japanese, Chinese, and Korean Pokémon to predict pre-evolution and post-evolution status.
Conducting an elicitation experiment where Japanese participants named novel Pokémon.
Comparing machine learning performance against human classification of elicited Pokémon names, incorporating name length as a feature and using a novel cross-validation method to address overfitting.

Main Results:

Machine learning models demonstrated efficient learning of systematic sound-meaning correspondence patterns.
The trained random forests achieved higher classification accuracy than human participants in distinguishing Pokémon evolutionary stages.
A novel cross-validation method was developed to resolve overfitting issues identified in initial experiments.

Conclusions:

Machine learning algorithms, particularly random forests, are adept at identifying and utilizing sound symbolism for classification tasks.
AI models can surpass human performance in recognizing subtle sound-meaning relationships within linguistic data.
The study validates the efficacy of sound symbolism in predicting semantic categories, even in artificial linguistic systems like Pokémon names.