<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="onoeva-2026-polar">
    <titleInfo>
        <title>Not All Polar Questions Are the Same: ASR, Humans, and Russian</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Onoeva</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nina</namePart>
            <namePart type="family">Hosseini-Kivanani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alessio</namePart>
            <namePart type="family">Brutti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Matassoni</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sandipana</namePart>
            <namePart type="family">Dowerah</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Davide</namePart>
            <namePart type="family">Liga</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christoph</namePart>
            <namePart type="family">Schommer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Word Error Rate (WER) remains the standard metric in automatic speech recognition (ASR) evaluation, yet it does not capture higher-level linguistic distinctions such as prosody. This article examines how three state-of-the-art open-source ASR models (Whisper, Meta’s MMS, and GigaAM) handle the distinction between Russian polar questions and assertions. Russian is particularly suitable for this investigation because polar questions can be marked either morphologically (li, razve) or purely intonationally, without changes in word order. Using audio stimuli from a controlled psycholinguistic experiment, I compare human classification performance in two experimental studies with ASR transcriptions, taking sentence-final punctuation as a proxy for prosodic interpretation. While human participants show near-ceiling accuracy, the ASR models perform inconsistently, especially on intonationally marked questions. Additional contextual cues improve performance in some cases but also reveal instability across conditions. The results demonstrate that evaluating punctuation provides insights beyond WER and allows a more fine-grained view of how current ASR systems encode prosodic and grammatical information.</abstract>
    <identifier type="citekey">onoeva-2026-polar</identifier>
    <identifier type="doi">10.63317/3qkbn7arv9es</identifier>
    <location>
        <url>https://aclanthology.org/2026.speakable-1.4/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>31</start>
            <end>38</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Not All Polar Questions Are the Same: ASR, Humans, and Russian
%A Onoeva, Maria
%Y Hosseini-Kivanani, Nina
%Y Brutti, Alessio
%Y Matassoni, Marco
%Y Dowerah, Sandipana
%Y Liga, Davide
%Y Schommer, Christoph
%S Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F onoeva-2026-polar
%X Word Error Rate (WER) remains the standard metric in automatic speech recognition (ASR) evaluation, yet it does not capture higher-level linguistic distinctions such as prosody. This article examines how three state-of-the-art open-source ASR models (Whisper, Meta’s MMS, and GigaAM) handle the distinction between Russian polar questions and assertions. Russian is particularly suitable for this investigation because polar questions can be marked either morphologically (li, razve) or purely intonationally, without changes in word order. Using audio stimuli from a controlled psycholinguistic experiment, I compare human classification performance in two experimental studies with ASR transcriptions, taking sentence-final punctuation as a proxy for prosodic interpretation. While human participants show near-ceiling accuracy, the ASR models perform inconsistently, especially on intonationally marked questions. Additional contextual cues improve performance in some cases but also reveal instability across conditions. The results demonstrate that evaluating punctuation provides insights beyond WER and allows a more fine-grained view of how current ASR systems encode prosodic and grammatical information.
%R 10.63317/3qkbn7arv9es
%U https://aclanthology.org/2026.speakable-1.4/
%U https://doi.org/10.63317/3qkbn7arv9es
%P 31-38

Markdown (Informal)

[Not All Polar Questions Are the Same: ASR, Humans, and Russian](https://aclanthology.org/2026.speakable-1.4/) (Onoeva, SPEAKABLE 2026)

• Not All Polar Questions Are the Same: ASR, Humans, and Russian (Onoeva, SPEAKABLE 2026)

ACL

• Maria Onoeva. 2026. Not All Polar Questions Are the Same: ASR, Humans, and Russian. In Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026, pages 31–38, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).