<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hsieh-2025-compact">
    <titleInfo>
        <title>A Compact Whisper+LoRA Baseline for Taiwanese Hakka ASR in FSR-2025</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hung-Ting</namePart>
        <namePart type="family">Hsieh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kai-Wei</namePart>
            <namePart type="family">Chang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ke-Han</namePart>
            <namePart type="family">Lu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chih-Kai</namePart>
            <namePart type="family">Yang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zhi-Rui</namePart>
            <namePart type="family">Tam</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wen-Yu</namePart>
            <namePart type="family">Chang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chung-Che</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">National Taiwan University, Taipei City, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-379-1</identifier>
    </relatedItem>
    <abstract>We present a compact baseline for the For- mosa Speech Recognition (FSR-2025) Tai- wanese Hakka ASR challenge. Our system fine-tunes Whisper-large-v2 (Track 1) and Whisper-large-v3-turbo (Track 2) (Radford et al., 2022) with LoRA (Hu et al., 2021), under a consistent normalization policy and balanced speaker-based dev splits. On the official warm-up set, we obtain 10.94% CER for Track 1 (Hanzi) and 28.48% SER for Track 2 (Pinyin). We provide simple, reproducible pipelines covering data prepa- ration, training, inference, and evaluation, without using external data or language models.</abstract>
    <identifier type="citekey">hsieh-2025-compact</identifier>
    <location>
        <url>https://aclanthology.org/2025.rocling-main.55/</url>
    </location>
    <part>
        <date>2025-11</date>
        <extent unit="page">
            <start>467</start>
            <end>470</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Compact Whisper+LoRA Baseline for Taiwanese Hakka ASR in FSR-2025
%A Hsieh, Hung-Ting
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F hsieh-2025-compact
%X We present a compact baseline for the For- mosa Speech Recognition (FSR-2025) Tai- wanese Hakka ASR challenge. Our system fine-tunes Whisper-large-v2 (Track 1) and Whisper-large-v3-turbo (Track 2) (Radford et al., 2022) with LoRA (Hu et al., 2021), under a consistent normalization policy and balanced speaker-based dev splits. On the official warm-up set, we obtain 10.94% CER for Track 1 (Hanzi) and 28.48% SER for Track 2 (Pinyin). We provide simple, reproducible pipelines covering data prepa- ration, training, inference, and evaluation, without using external data or language models.
%U https://aclanthology.org/2025.rocling-main.55/
%P 467-470

Markdown (Informal)

[A Compact Whisper+LoRA Baseline for Taiwanese Hakka ASR in FSR-2025](https://aclanthology.org/2025.rocling-main.55/) (Hsieh, ROCLING 2025)

• A Compact Whisper+LoRA Baseline for Taiwanese Hakka ASR in FSR-2025 (Hsieh, ROCLING 2025)

ACL

• Hung-Ting Hsieh. 2025. A Compact Whisper+LoRA Baseline for Taiwanese Hakka ASR in FSR-2025. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 467–470, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.