@inproceedings{liang-levow-2025-tone,
title = "Tone in Perspective: A Computational Typological Analysis of Tone Function in {ASR}",
author = "Liang, Siyu and
Levow, Gina-Anne",
editor = "Hahn, Michael and
Rani, Priya and
Kumar, Ritesh and
Shcherbakov, Andreas and
Sorokin, Alexey and
Serikov, Oleg and
Cotterell, Ryan and
Vylomova, Ekaterina",
booktitle = "Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sigtyp-1.11/",
doi = "10.18653/v1/2025.sigtyp-1.11",
pages = "82--92",
ISBN = "979-8-89176-281-7",
abstract = "This study investigates the impact of pitch flattening on automatic speech recognition (ASR) performance across tonal and non-tonal languages. Using vocoder-based signal processing techniques, we created pitch-flattened versions of speech recordings and compared ASR performance against original recordings. Results reveal that tonal languages experience substantially larger performance degradation than non-tonal languages. Analysis of tone confusion matrices shows systematic patterns of misidentification where contour tones collapse toward level tones when pitch information is removed. Calculation of tone{'}s functional load at syllable and word levels demonstrates that syllable-level functional load strongly predicts ASR vulnerability to pitch flattening, while word-level patterns reflect each language{'}s morphological structure. These findings illuminate the differential importance of pitch information across languages and suggest that ASR systems for languages with high syllable-level functional load require more robust pitch modeling."
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<abstract>This study investigates the impact of pitch flattening on automatic speech recognition (ASR) performance across tonal and non-tonal languages. Using vocoder-based signal processing techniques, we created pitch-flattened versions of speech recordings and compared ASR performance against original recordings. Results reveal that tonal languages experience substantially larger performance degradation than non-tonal languages. Analysis of tone confusion matrices shows systematic patterns of misidentification where contour tones collapse toward level tones when pitch information is removed. Calculation of tone’s functional load at syllable and word levels demonstrates that syllable-level functional load strongly predicts ASR vulnerability to pitch flattening, while word-level patterns reflect each language’s morphological structure. These findings illuminate the differential importance of pitch information across languages and suggest that ASR systems for languages with high syllable-level functional load require more robust pitch modeling.</abstract>
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%0 Conference Proceedings
%T Tone in Perspective: A Computational Typological Analysis of Tone Function in ASR
%A Liang, Siyu
%A Levow, Gina-Anne
%Y Hahn, Michael
%Y Rani, Priya
%Y Kumar, Ritesh
%Y Shcherbakov, Andreas
%Y Sorokin, Alexey
%Y Serikov, Oleg
%Y Cotterell, Ryan
%Y Vylomova, Ekaterina
%S Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-281-7
%F liang-levow-2025-tone
%X This study investigates the impact of pitch flattening on automatic speech recognition (ASR) performance across tonal and non-tonal languages. Using vocoder-based signal processing techniques, we created pitch-flattened versions of speech recordings and compared ASR performance against original recordings. Results reveal that tonal languages experience substantially larger performance degradation than non-tonal languages. Analysis of tone confusion matrices shows systematic patterns of misidentification where contour tones collapse toward level tones when pitch information is removed. Calculation of tone’s functional load at syllable and word levels demonstrates that syllable-level functional load strongly predicts ASR vulnerability to pitch flattening, while word-level patterns reflect each language’s morphological structure. These findings illuminate the differential importance of pitch information across languages and suggest that ASR systems for languages with high syllable-level functional load require more robust pitch modeling.
%R 10.18653/v1/2025.sigtyp-1.11
%U https://aclanthology.org/2025.sigtyp-1.11/
%U https://doi.org/10.18653/v1/2025.sigtyp-1.11
%P 82-92
Markdown (Informal)
[Tone in Perspective: A Computational Typological Analysis of Tone Function in ASR](https://aclanthology.org/2025.sigtyp-1.11/) (Liang & Levow, SIGTYP 2025)
ACL