Laeli Fajriyah, Almira Agwinanda, Ria Riski Marsuki

Analysis of Tone Pronounciation in Beginner-Level Students of Mandarin Language Education Program at Semarang State University (UNNES) Using PRAAT Program Assistance

Introduction

Analysis of tone pronounciation in beginner-level students of mandarin language education program at semarang state university (unnes) using praat program assistance. Analyze Mandarin tone pronunciation errors in beginner UNNES students using PRAAT. Identifies specific challenges with tones 1, 2, and 4, offering insights for language tech development.

Abstract

Mandarin Chinese is one of the languages that uses a tonal system, errors in tone pronunciation can lead to differences in meaning. Pronunciation differences can be directly perceived by experts, but with the advancement of technology today, detailed differences can be observed through sound processing programs or applications. This study uses PRAAT as an auxiliary sound processing tool, which can display pitch curves of voices and show the frequencies used to analyze tone pronunciation. The subjects of this study are undergraduate students from the class of 2024 of the Mandarin Language Education program at UNNES, at the beginner level. The study uses a descriptive quantitative approach, with the tone pronunciation analysis results of native speakers used as a comparison to the students' tone pronunciations. Data collection techniques in this research include observation, recording field notes, and documentation. The results of this study show that 19.19% of students pronounced tone 1 with a rising frequency, 22.7% of students pronounced tone 2 with a low frequency difference, making it sound similar to tone 1, and more than 93% of students pronounced tone 4 with a high frequency spike. This research is expected to provide a reference for developers of technology-based language learning applications to improve pronunciation analysis features. In addition, it is hoped that this research can contribute to the field of phonetics and serve as a reference for similar studies.

Review

This study addresses a highly relevant and challenging aspect of Mandarin Chinese acquisition: tone pronunciation. The choice to focus on beginner-level students at UNNES using a quantitative, descriptive approach is appropriate for identifying common error patterns. A key strength of the research lies in its methodological precision, leveraging the PRAAT program as an objective sound processing tool. This technological assistance allows for detailed analysis of pitch curves and frequencies, moving beyond subjective perception to provide concrete data for identifying pronunciation deviations, which is a commendable approach in phonetic research and language education. The comparison against native speaker pronunciations provides a solid benchmark for evaluating student performance. The findings presented are particularly insightful, detailing specific common errors among the target demographic. Identifying that nearly one-fifth of students pronounced Tone 1 with a rising frequency, a significant portion pronounced Tone 2 with a low-frequency difference making it sound like Tone 1, and over 93% of students exhibited a high-frequency spike for Tone 4, offers valuable empirical data. These specific error patterns are crucial for informing pedagogical strategies and developing targeted interventions. The potential impact on the development of technology-based language learning applications, particularly in enhancing pronunciation analysis features, is a practical and forward-looking contribution, demonstrating the study's relevance beyond pure academic inquiry. While the abstract effectively outlines the study's scope and key findings, the full paper would benefit from elaborating on several points. A clearer indication of the sample size (number of students in the class of 2024) would strengthen the generalizability of the reported percentages. Furthermore, while the *what* of the errors is well-articulated, a deeper discussion on the *why*—potential underlying causes such as first language interference or specific pedagogical challenges—would enrich the analysis significantly. Detailing the specific data collection protocols using PRAAT, such as the type of speech samples analyzed (e.g., isolated tones, words, sentences) and recording conditions, would also enhance the study's methodological transparency and replicability for future similar investigations.

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