Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning
Authors: Mingfei Lau, Qian Chen, Yeming Fang, Tingting Xu, Tongzhou Chen, Pavel Golik
Accepted at: acl25

Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning

Mingfei Lau    Qian Chen    Yeming Fang    Tingting Xu    Tongzhou Chen    Pavel Golik
Google
{laufei, allenqc, yeming, xutt, tongzhou, golik}@google.com

Abstract

Our quality audit for three widely used public multilingual speech datasets—Mozilla Common Voice 17.0, FLEURS, and VoxPopuli—shows that in some languages, these datasets suffer from significant quality issues. We believe addressing these issues will make these datasets more useful as training and evaluation sets, and improve downstream models. We divide these quality issues into two categories: micro-level and macro-level. We find that macro-level issues are more prevalent in less institutionalized, often under-resourced languages. We provide a case analysis of Taiwanese Southern Min (nan_tw) that highlights the need for proactive language planning (e.g. orthography prescriptions, dialect boundary definition) and enhanced data quality control in the process of Automatic Speech Recognition (ASR) dataset creation. We conclude by proposing guidelines and recommendations to mitigate these issues in future dataset development, emphasizing the importance of sociolinguistic awareness in creating robust and reliable speech data resources.

1 Introduction

The emergence of massively multilingual speech datasets has significantly advanced the performance of various speech technologies in recent years, particularly for low-resource languages. These datasets are crucial for training and evaluating state-of-the-art ASR models like Whisper (Radford et al., 2023), Google USM (Zhang et al., 2023), SeamlessM4T (Barrault et al., 2023), MMS (Pratap et al., 2024), and Gemini (Gemini Team et al., 2024), and also enable advances in cross-lingual speech representation learning (Babu et al., 2022; Conneau et al., 2021) and downstream applications like multilingual speech generation and understanding (Le et al., 2023; Rubenstein et al., 2023). However, despite their growing importance, the quality of these datasets remains surprisingly under-researched.

Prior work on data collection and curation (Penedo et al., 2024; Goyal et al., 2022; Kreutzer et al., 2022) has acknowledged the generally lower quality of web-scraped data, but these efforts primarily focused on text. Similarly, research on ASR data augmentation for low-resource languages (Casanova et al., 2023; Bartelds et al., 2023; Tsoukala et al., 2023) has not addressed data quality issues adequately. The community-driven Mozilla Common Voice project (Ardila et al., 2020), for example, lacks well-documented quality control processes for its diverse text sources (Wikipedia and volunteer contributions) and subsequent audio recordings, which makes the quality and reliability largely unknown.

Inspired by Kreutzer et al. (2022)'s audit methodology for text datasets, we conduct a thorough quality assessment of three widely used multilingual speech datasets: Mozilla Common Voice 17.0 (MCV17, Ardila et al. 2020), FLEURS (Conneau et al., 2023), and VoxPopuli (Wang et al., 2021). Our investigation employs both quantitative and qualitative methods. We calculate metrics including Signal-to-Noise Ratio (SNR), Voice Activity Detection (VAD), median utterance duration, and median word count for each language subset. Qualitatively, we asked native speaker volunteers, covering around 40 languages detailed in Table 16 in Appendix, to review 100 randomly sampled sentences (text and audio) for coherence, audio-text alignment, dialect, topic domain, and language classification from each language subset of the datasets.

Our analysis reveals serious data quality issues, particularly in low-resource, less-institutionalized languages (Sections 3 and 4). As one example, the nan_tw (Taiwanese Southern Min) subset in MCV17 showcases a multitude of these issues, rendering it nearly unusable without significant cleaning or restructuring (Section 5). We find a strong
positive correlation between a language’s institutionalization status and its dataset quality, a crucial factor often overlooked in ASR research. We discuss the impact of these issues on downstream research and applications, and propose mitigation guidelines (Section 6). We draw conclusions and suggest future work in Section 7.

2 Datasets

In this work, we study three multilingual datasets: Mozilla Common Voice 17.0 (MCV17), FLEURS, and VoxPopuli.

• Mozilla Common Voice (Ardila et al., 2020) is a community-driven project proposed by Mozilla. Sentence sourcing, recording and reviewing are all contributed by volunteers. Version 17 supports 124 locales.

• FLEURS (Conneau et al., 2023) uses the text from the machine translation corpus FLoRes-101 (Goyal et al., 2022). Sentences are extracted from the English Wikipedia and translated to 101 languages by professional translators. Each sentence is recorded by 3 native speakers and invalid recordings are discarded.

• VoxPopuli (Wang et al., 2021) uses speech and transcriptions from European Parliament event recordings. The paired data contains 16 European languages.

3 Micro-Level Issues

Micro-level issues in ASR datasets typically stem from inadequate quality control, supervision, or management during data collection and curation. They are often detectable via automatic metrics and can be mitigated programmatically. Micro-level issues are language-agnostic and may exist in any language subsets.

3.1 Extremely Short Duration

In VoxPopuli the median utterance duration ranges from around 6 to 13 seconds and FLEURS ranges from 9 to 24 seconds. However, MCV17 displays a concerning trend of extremely short utterances. Detailed plots are shown in Appendix C. In MCV17, 35 languages have median utterance duration under 4 seconds. And for most languages, over 99% of the utterances are under 10 seconds. We also find some extreme cases such as nan_tw (Taiwanese Southern Min), sr (Serbian), and br (Breton), whose median utterance durations are below 3 seconds, and 99% of the utterances are below 7 seconds. We manually inspect those language subsets and discover that the text prompts of these languages are mostly short phrases or isolated words, as shown in Figure 4. Previous research has discovered that ASR and TTS models may fail to generalize to inputs of lengths not seen in training (Narayanan et al., 2019; Varis and Bojar, 2021; Chiu et al., 2021; Mengke et al., 2024). Without this insight, a model trained on MCV17 nan_tw might fail on long-form tasks like video subtitling. While mitigation is possible at the level of model architecture (e.g. more flexible attention variants), we believe this needs to be fixed at the dataset design level, as Common Voice does not enforce a strict length requirement for contributed text prompts, leading to the ingestion of low quality inputs.

3.2 Low Proportion of Speech

To assess the proportion of actual speech content within the audio data, we employed a neural model to classify speech and non-speech segments in all three datasets. While all 14 languages in VoxPopuli have at least 89% speech, we identified several languages in other datasets with significantly lower proportions, detailed in Appendix D. Specifically, Basaa (bas), Zaza (zza), Serbian (sr) in MCV17, and Danish (da_dk) in FLEURS exhibiting less than 50% speech content.. We manually inspected these languages and found two primary causes:

• The speaker’s voice is too distant from the microphone, resulting in poor audio capture (e.g. the da_dk training set in FLEURS).

• The short text prompt issue described in Section 3.1 reduces the proportion of actual speech in the utterance, resulting in an increased proportion of silence between the start and stop of the recording process, as contributors interacted with their recording devices.

Similar issues have also been reported by the public1 which further validates our findings. This issue combined with extremely short utterance durations severely limits the amount of usable speech data. For instance, while nan_tw in MCV17 contains 21 hours of audio, only 48.3% constitutes actual

1A large number of incorrect audio samples on FLEURS reported in April 2023 https://huggingface.co/datasets/google/fleurs/discussions/16
speech, resulting in merely 10 hours of usable data. While in general this micro-level issue affects TTS more than ASR, using the raw audio duration e.g. for re-balancing the amounts of training data per language can lead to unexpected results. This can be mitigated by using the number of transcribed words (or tokens) as the most fundamental unit for quantifying speech data.

3.3 Imbalanced Topic Domains
Assessing topic domain balance is challenging to quantify programmatically. Therefore, we adopted a qualitative approach, sampling text prompts and corresponding audio data from each dataset. We asked native speakers to evaluate whether these sentences were representative of typical, everyday conversations—a common target domain for general-purpose ASR systems. However, we acknowledge that other domains (e.g., news broadcasts, academic lectures) might be relevant for specific applications. This investigation revealed significant topic domain imbalances in several languages within both MCV17 and FLEURS, particularly concerning the assumption of everyday conversation as the target. In FLEURS, the reliance on text prompts from the FLORes-101 dataset (Goyal et al., 2022), ultimately sourced from Wikipedia, leads to a dominance of formal, literary, and encyclopedic sentences.

We also identified an issue in MCV17, where a number of sentences across multiple languages exhibit high repetitiveness, often lacking meaningful content and suggesting machine generation using fixed templates (see Table 11 in Appendix). It’s important to acknowledge that these datasets are built on the valuable contributions of volunteers. Given the community-driven nature of text prompt creation in Common Voice, it is possible that these repetitive sentences were introduced due to varying interpretations of the guidelines or a lack of awareness about their potential impact on model training. This highlights the need for clearer guidelines and more robust review processes within community-driven projects.

The problem of domain mismatch has been discussed in the context of monolingual datasets in (Likhomanenko et al., 2021). In multilingual datasets, a poor coordination in topic selection across languages might lead to incorrect interpretation of ASR and TTS model quality. While there is no easy mitigation, at the very least this information should be exposed in machine-readable metadata.

3.4 Lack of Speaker Diversity
The final micro-level issue we identified is a lack of speaker diversity. We calculated the average recording time per speaker for each language in MCV17, as detailed in Tables 13 and 14 in the Appendix. Notably, Macedonian (mk) exhibits an average of 1.20 hours of audio per speaker, with only 19 unique speakers in total, indicating a high concentration of data from a limited number of contributors. Data of some languages such as Zulu (zu), Northern Sotho (nso), Haitian Creole (ht) consist of recordings from only a single speaker.

This introduces a high risk of overfitting to the speakers’ age, gender and dialects, as well as a risk of increasing bias in downstream models (Koe-necke et al., 2020). Whenever the speaker statistics are available as metadata, dataset users can focus on the subset of languages with diverse data, which produce reliable results.

4 Macro-Level Issues
Macro-level issues often arise from overlooking a language’s sociolinguistic context during dataset design. These issues are typically not detectable through automatic metrics, requiring manual inspection and linguistic expertise for diagnosis. They are particularly prevalent in less-institutionalized languages, often characterized by complex phenomena like digraphia or diglossia. Our analysis of VoxPopuli, which primarily includes more-institutionalized languages, further supports this claim, as it revealed no such macro-level issues.

4.1 Unspecified Writing System in Digraphic Languages
Digraphia is a sociolinguistic phenomenon where a single language is written using multiple writing systems (Dale, 1980). Contemporary examples include Serbian (Latin and Cyrillic scripts), Malay (Latin and Jawi scripts), and Punjabi (Gurmukhi and Shahmukhi scripts). Jung and Kim (2023) further categorize digraphia into two types: complementary and exclusive. Japanese is an well-known example of complementary digraphia, where four scripts, Kanji, Hiragana, Katakana, and Latin letters are combined in common Japanese texts. In ASR, exclusive digraphia introduces multiple parallel written forms that can create ambiguities in audio transcription at the script level, while complementary digraphia can cause ambiguities at the or-
thography / spelling level (Mazovetskiy and Kudo, 2024).

For ASR, the definition of "writing system" extends beyond script to include orthography and spelling rules. Minor variations such as "color" vs. "colour" in American and British English can impact evaluations based on Word Error Rate (WER), where a spelling difference counts as a word error. While these variations have a limited effect in English due to their infrequency and can be addressed with rule-based normalization, languages with multiple coexisting spelling standards for substantial portions of their vocabulary will face significantly greater challenges, as discussed below.

4.1.1 Norwegian Bokmål / Nynorsk

Norwegian exemplifies a digraphic language with two synchronic written standards: Bokmål (nb_no) and Nynorsk (nn_no). Table 1 illustrates the differences with an example sentence. Notably, 4 out of 8 words differ in spelling, including common words like jeg (Bokmål) and eg (Nynorsk), both meaning "I" in English. Both MCV17 and FLEURS include Norwegian, but Common Voice uses nn_no (purportedly Nynorsk) while FLEURS uses nb_no (purportedly Bokmål). To verify the actual proportions of Bokmål and Nynorsk in these subsets, we employed a classification script detailed in Algorithm 1.

<table>
  <tr>
    <th>English</th>
    <td>Have I covered myself with song and playing the harp.</td>
  </tr>
  <tr>
    <th>Bokmål</th>
    <td>Har eg dekt meg med song og harpspel.</td>
  </tr>
  <tr>
    <th>Nynorsk</th>
    <td>Har jeg dekkt meg med sang og harpspill.</td>
  </tr>
</table>

Table 1: Spelling differences between Bokmål and Nynorsk. In this example, a mismatching orthography of the same sentence will lead to a 50% WER even the transcription is completely "correct".

<table>
  <tr>
    <th></th>
    <th>MCV17 nn_no</th>
    <th>FLEURS nb_no</th>
  </tr>
  <tr>
    <th>Nynorsk (nn_no)</th>
    <td>764 (65.1%)</td>
    <td>323 (8.8%)</td>
  </tr>
  <tr>
    <th>Bokmål (nb_no)</th>
    <td>96 (8.1%)</td>
    <td>2682 (72.7%)</td>
  </tr>
  <tr>
    <th>Mixed</th>
    <td>161 (13.7%)</td>
    <td>344 (9.3%)</td>
  </tr>
  <tr>
    <th>Unmarked</th>
    <td>153 (13.0%)</td>
    <td>338 (9.1%)</td>
  </tr>
  <tr>
    <th>Total sentences</th>
    <td>1174 validated</td>
    <td>3687</td>
  </tr>
</table>

Table 2: Classification of Norwegian text prompts in MCV17 and FLEURS.

<table>
  <tr>
    <th>WER [%]</th>
    <th>MCV17 nn_no</th>
    <th>FLEURS nb_no</th>
  </tr>
  <tr>
    <th>Total</th>
    <td>49.1</td>
    <td>23.8</td>
  </tr>
  <tr>
    <th>Del/Ins/Sub</th>
    <td>11.8 / 1.6 / 35.0</td>
    <td>11.1 / 2.2 / 10.0</td>
  </tr>
</table>

Table 3: WER of a Norwegian Bokmål Conformer ASR model on MCV17 and FLEURS test splits. When tested on Nynorsk data, the substitution errors increase by 25% abs.

While the model exhibits similar deletion and insertion error rates across both datasets, the substitution error rate is nearly 25% higher on MCV17’s nn_no subset. Manual inspection of the substitution errors revealed that most are indeed orthographic variants from Bokmål. This supports our hypothesis that mixing different writing systems in an ASR dataset significantly impacts downstream evaluations.

4.1.2 Risks of Unverified Script Assumptions in Digraphic Languages

Digraphia is not a static structure; it evolves, sometimes rapidly. The "default" script for a given language can shift, as is currently occurring in several post-Soviet countries (Jung and Kim, 2023). For instance, Kazakhstan is transitioning from Cyrillic to Latin script by 2025, and Mongolia is restoring the Mongolian (Bichig) script by 2025 according to the government’s plan. Neither MCV17 nor FLEURS include script codes in their locale codes, leading to implicit, unverified assumptions about the script used in the text data. These assumptions, listed in Table 15 in Appendix, introduce significant risks for downstream applications, potentially compromising dataset usability in the near future.

4.2 Ambiguous Register or Variety in Diglossic Languages

Diglossia describes a community’s use of two distinct language varieties in a compartmentalized manner: a "High" (H) variety for typically formal contexts and a "Low" (L) variety for everyday conversation, with the community perceiving these varieties as a single language (Ferguson, 1996). This duality may lead to ambiguous language code
interpretations in ASR dataset construction. For example, "I speak and write Chinese" could imply "I speak and write Mandarin", or "I speak Cantonese and write Mandarin". Such ambiguity can result in datasets pairing audio and text from mutually unintelligible varieties. Contemporary examples of diglossic languages include Standard Arabic (Fusha) and its vernacular dialects (Brustad, 2017; Ferguson, 1996), Hong Kong Chinese / Cantonese (Snow, 2010), Standard German / Swiss German, Classical Tibetan / vernacular Tibetan (Roche, 2017), Persian (Mahmoodi-Bakhtiari, 2018) and Bengali (Dil, 1986). Our analysis of MCV17 and FLEURS revealed serious issues with register and variant confusion, particularly in Arabic and Hong Kong Chinese / Cantonese. Since VoxPopuli does not include any diglossic languages, we find no such issues in VoxPopuli.

4.2.1 Arabic

Modern Standard Arabic (MSA, or Fusha) is a classical example of diglossia (Ferguson, 1959), employed in formal contexts such as religion and education, while regional variants (e.g. Masri, Dārija) prevail in everyday conversation. These dialects can differ significantly from MSA (Hoigilt and Mejdeli, 2017). We analyzed the Arabic text prompts in MCV17 (ar) and FLEURS (ar_eg) using a classification tool (Algorithm 2 in Appendix). Table 4 shows that both datasets mainly contain MSA, with some dialectal Arabic or mixed forms in MCV17’s ar subset. Interestingly, FLEURS, labeled ar_eg (presumably Egyptian Arabic), contains almost exclusively MSA. This could be due to a combination of factors: the intended dataset composition, the selection of source material, and the transcribers’ interpretation of the term "Arabic", which can be ambiguous in a diglossic context. This highlights the complex interplay between dataset composition, content selection, and transcribers’ preference and understanding of the task in diglossic situations.

<table>
  <tr>
    <th></th>
    <th>MCV17 ar</th>
    <th>FLEURS ar_eg</th>
  </tr>
  <tr>
    <td>MSA (Fusha)</td>
    <td>5963 (76.3%)</td>
    <td>2787 (98.6%)</td>
  </tr>
  <tr>
    <td>Dialect</td>
    <td>991 (1.2%)</td>
    <td>0</td>
  </tr>
  <tr>
    <td>Mixed</td>
    <td>1648 (2.1%)</td>
    <td>25 (0.88%)</td>
  </tr>
  <tr>
    <td>Unmarked</td>
    <td>15881 (20.3%)</td>
    <td>15 (0.53%)</td>
  </tr>
  <tr>
    <td>#sentences</td>
    <td>78157 validated</td>
    <td>2827</td>
  </tr>
</table>

Table 4: Classification of text prompts in MCV17 ar and FLEURS ar_eg. Nearly all data of FLEURS ar_eg is in Fusha.

We speculate that the prevalence of MSA in FLEURS, despite the ar_eg label, might be partly a consequence of adopting a strict language code formatting standard that does not adequately represent the nuances of diglossic languages. While regional codes exist within the ISO 639-3 standard, there is no widely accepted code for MSA within the ISO 3166-1 alpha-2 country code framework, which is often used in conjunction with language codes to form locale identifiers. The dataset creators might have chosen eg (Egypt) as the most readily available option to satisfy the IETF BCP 47 language tag specifications, despite it not accurately reflecting the linguistic reality of MSA as a supra-regional standard. This serves as a microcosm of a broader challenge: the limitations of current language classification systems (particularly within ISO 639-3) in representing regional, supra-regional and diglossic varieties, especially those that do not align neatly with national boundaries. For instance, there is no distinct code for African American Vernacular English (AAVE) or Scottish English within the current framework, hindering the ASR and TTS system development tailored to these communities.

4.2.2 Hong Kong Chinese / Cantonese

Hong Kong presents another case of diglossia (Snow, 2010), where Standard Written Chinese (SWC), a written register largely based on Modern Standard Mandarin, is used in formal writing and spoken Cantonese is used in everyday conversation. According to Unicode CLDR version 44.0,2 zh is interpreted as "Chinese", referring to SWC, while Cantonese has its own ISO 639-3 code, yue. However, the ambiguous nature of the code zh often leads to inconsistent interpretations in the industry.

We used the canto-filter package3 developed by Lau et al. (2024) to classify text prompts into four categories. The results, shown in Table 5, highlight significant inconsistencies across datasets. Common Voice’s yue subset aligns relatively well with Written Vernacular Cantonese (WVC), with most prompts being in WVC. However, Common Voice’s zh_hk subset contains a mixture of SWC and Cantonese, while FLEURS’ yue_hk subset consists almost entirely of SWC. This suggests that the yue_hk label in FLEURS is likely a misnomer and should actually be zh_hk.

2Territory-Language Information
3pypi.org/project/canto-filter/
<table>
  <tr>
    <th></th>
    <th>MCV17 zh_hk</th>
    <th>MCV17 yue</th>
    <th>FLEURS yue_hk</th>
  </tr>
  <tr>
    <td>SWC</td>
    <td><b>8851</b> (9.6%)</td>
    <td>15 (0.1%)</td>
    <td><b>2803</b> (89.8%)</td>
  </tr>
  <tr>
    <td>Cantonese</td>
    <td>37357 (40.3%)</td>
    <td>16466 (75.7%)</td>
    <td>0</td>
  </tr>
  <tr>
    <td>Mixed</td>
    <td>299 (0.3%)</td>
    <td>40 (0.2%)</td>
    <td>0</td>
  </tr>
  <tr>
    <td>Unmarked</td>
    <td>46113 (49.8%)</td>
    <td>5238 (24.1%)</td>
    <td>317 (10.2%)</td>
  </tr>
  <tr>
    <td>#sentences</td>
    <td>92620</td>
    <td>21759</td>
    <td>3120</td>
  </tr>
</table>

Table 5: Classification of text prompts in MCV17 yue, zh_hk, and FLEURS yue_hk. None of the FLEURS yue_hk is Cantonese.

4.3 Ambiguous Scoping of Target Dialect Continuum

Both MCV17 and FLEURS utilize ISO 639-3 codes to identify languages. However, neither dataset specifies the dialect scope of each language subset in the metadata, leaving the interpretation of these codes open to contributors. Our investigation revealed that the ff_sn (Fula, Senegal) subset in FLEURS only includes the Peul dialect spoken across Senegal. This is significant because the the Guinean variant of Fula has the most speakers, not the one found in the dataset. Another case we identify is the kea_cv (Cape Verdean Creole, a.k.a. Kabuverdianu) in FLEURS, which consists entirely of the Sotavento (Southern Islands) variant. The omission of the Barlavento (Northern Islands) variant could limit the dataset’s usefulness for developing speech systems that are robust across the entire dialect continuum of Cape Verdean Creole.

The ambiguity in dialect scoping can skew the representativeness of the target language dataset as a whole. Researchers and developers might assume that a dataset labeled with a particular code encompasses the full range of variation within that language, when in reality it might only represent a limited subset of dialects. This can result in inflated performance metrics if ASR models are evaluated only on the dialects present in the training data, while performance on other dialects remains unknown and potentially lower (Koenecke et al., 2020; Tang et al., 2021; Talafha et al., 2024). In TTS, having no control over the exact dialect might strike some sensitive nerve among native speakers, especially when the dialect boundaries align with historical and sociopolitical injustices.

5 An Extreme Case Analysis: MCV17 nan_tw (Taiwanese Southern Min)

5.1 Social and Historical Context

Taiwanese Southern Min (TSM, a.k.a. Taiwanese Hokkien) has historically been an unwritten language. Various writing systems emerged sporadically, including full Sinograph (a.k.a. Chinese characters or Han script), Tâi-lô romanization, Church romanization (Pe'hi-oe-jî), and a mixture of Sinographs and romanization (Ota, 2005; Alvin, 1999). While the Taiwan Ministry of Education introduced the Taiwanese Southern Min Recommended Characters4 between 2007 and 2009, and a Dictionary of Frequently-Used Taiwan Minnan5 to promote a standard orthography using full Sinographs, as of early 2025, the language community has not yet reached a consensus. As examples, a substantial proportion of pages of Southern Min Wikipedia use Latin letters, while everyday usage on social media platforms like Threads (Figure 8 in Appendix) displays a varied mix of Sinographs and romanizations. Unlike the more established complementary digraphia in Japanese, TSM lacks widely adopted rules for script choice, resulting in a more undetermined and dynamic case of complementary digraphia.

5.2 Data Quality Issues

Table 12 in Appendix presents a snapshot of the nan_tw subset in MCV17, revealing critical issues that severely compromise its usability:

1. Dictionary Structure: The dataset resembles a dictionary dump, with nearly every entry consisting of single words or short phrases, rather than complete sentences.

2. Duplicate Text Prompts: Each prompt is written in both full Sinograph and full Latin (Tâi-lô), resulting in redundant entries.

3. Text-Audio Misalignment: Voice contributors apparently read each prompt (single words or phrases) only once, leading to misalignment between the text (containing both writing systems) and the corresponding audio.

Furthermore, many validated sentences (text prompts not yet paired with audio) are in zh_cn (Mandarin Chinese in simplified characters), posing a significant risk of language contamination and potentially introducing the "mixed-language" issues discussed in Section 4.2.2.

5.3 Root Causes of the nan_tw Data Issues

To understand the rationale behind the dictionary-style structure and dual-script representation in

4臺灣閩南語推薦用字700字表
5教育部臺灣台語常用詞辭典
MCV17 nan_tw, we contacted the voice and text contributors. They explained that most TSM speakers are not proficient in reading and writing TSM. Moreover, among those who are literate in the language, preferences and proficiency levels for different writing standards vary considerably. Some are only comfortable with Sinographs, others with Romanization, while some prefer a mixture of both. To maximize participation and facilitate data collection, the contributors opted to include both Sinographs and Romanization, so that all potential contributors could read the prompts.

This situation highlights a common challenge faced by many low-resource or less-institutionalized languages when developing ASR datasets. ASR fundamentally involves transcribing audio, the spoken form, into text, the written form. But what if the language has no "written form"? The motivations of building ASR for such languages may differ fundamentally from those with well-established written traditions, necessitating distinct approaches to dataset construction. We will explore their implications in Section 6.2.

6 Discussions

6.1 Impacts on Downstream Research and Applications

The quality issues discussed previously have significant ramifications for downstream research and applications. Shorter utterance lengths correlate with higher WER (Li et al., 2023, 2022), and excessive silence negatively impacts emotion recognition (Perez et al., 2022). Lack of speaker and topic diversity can introduce biases related to gender, age, and regional accents (Feng et al., 2024; Garnerin et al., 2021). While micro-level issues are often detectable programmatically, macro-level issues, stemming from sociolinguistic factors, can have more insidious and far-reaching impacts.

A compelling example is presented in Costa-jussà et al. (2022), where a language identification (LangID) system, evaluated on the FLoRes-200 dataset (the text source for FLEURS), failed to distinguish between zh_hk (Hong Kong Chinese) and yue (Cantonese), see the third confusion matrix in Figure 9 in the original paper. This stems exactly from the "wrong language" issue in FLEURS' yue_hk subset, which predominantly contains SWC rather than Cantonese, as discussed in Section 4.2.2 and publicly noted.6 Consequently, downstream models like Whisper-v3 struggle with these languages, exhibiting inconsistent outputs and unpredictable "auto-translations" between varieties.7 Furthermore, model distillation can exacerbate these issues, as seen in the WER degradation from 10.8% to 46.1% on Common Voice 15.0 yue.8

This problem is covert because when training and test data are in the same SWC variety, the wrong output language might not be reflected by the WER, while downstream users expect the transcriptions to be in WVC consistently. The experiments from Xie and Chen (2025) further validates our findings, where the outputs of Whisper models are in the completely wrong register, and model performance significantly improved when fine-tuned with the pure Cantonese data.

6.2 Addressing Macro-Level Issues: The Role of Language Planning in Speech Dataset Creation

The macro-level issues identified in this study, unlike micro-level issues that can be mitigated programmatically (e.g. Rai et al. (2024)), necessitate more fundamental improvements to dataset creation, particularly for less-institutionalized languages. We argue that it is crucial to incorporate language planning principles into the speech dataset design process.

Many widely spoken languages such as English, Spanish, Mandarin, are institutionalized and standardized due to a process called language planning (Cooper, 1989). This typically involves language planning agencies (LPAs), e.g. a country's education ministry, establishing a normative orthography, grammar, and lexicon to guide a diverse speech community. Consequently, speakers of such languages have good understanding of how to read and write them "correctly". However, with over 7,000 spoken languages and only around 200 countries in the world, most languages have not enjoyed such a privilege, leaving speakers without knowledge of how to read and write their mother tongues.

The decentralized, community-driven nature of Common Voice, while valuable for participation and diversity (Ardila et al., 2020), can exacerbate this issue. It can lead to implicit language planning decisions being made by communities without adequate expertise or consensus, as exemplified by the decision of merging Norwegian Nynorsk and

6 github.com/facebookresearch/flores/issues/61
7 github.com/openai/whisper/discussions/366
8 github.com/openai/whisper/discussions/2363
Bokmål.9 As Common Voice expands to more languages with complex sociolinguistic backgrounds, such as Konkani,10 we anticipate a rise in these macro-level issues without proactive intervention.

Therefore, we propose the following checklist to follow when creating a new massively multilingual speech dataset, especially for less-institutionalized languages:

1. Sociolinguistic Assessment: Before dataset creation, conduct a thorough sociolinguistic survey of the target language, including demographics, literacy rates, writing systems, diglossia/digraphia, language ideologies, and other relevant factors.

2. Language Planning in Dataset Design: If the language is not institutionalized (e.g. lacking a writing standard, written language is not vernacularized, multiple registers or scripts co-exist), revisit the dataset design goal and clarify the objective. E.g. is the dataset in the vernacular or formal register, in Latin alphabet or the indigenous alphabet? Determine such design goals collaboratively with linguists, community members and native speakers to ensure downstream acceptance and practicality.

3. Proactive Prescription and Detailed Guidance: Provide detailed transcription or recording guidelines to dataset contributors and annotators. Especially when the literacy rate is low or a standard orthography is lacking, prescribe specific guidelines on orthography, script and register choices to ensure annotators contribute in a consistent format.

4. Multi-Level Quality Assurance: Implement rigorous quality assurance on both the text and audio. This includes checks based on automatic metrics (e.g., rejecting silent or extremely short audio, and text in wrong scripts) and human evaluation (e.g. rejecting text or audio in the wrong register, or in an out-of-scope dialect).

5. Comprehensive and Transparent Metadata: Release the dataset with detailed metadata including the language planning decisions made in step 2. We recommend following the data statement practice by Bender and Friedman (2018).

When a language lacks a widely adopted written form, the motivation of developing ASR systems may shift from building a tech product to a tool for corpus planning and educational initiatives. As demonstrated by Williams et al. (2000) and Kumar et al. (2012), ASR can be employed to reduce illiteracy and promote the use of a standard written form within a community.

Previous research has pointed out the effectiveness of social network and media technologies in codifying polycentric orthography standards (Leggio and Matras, 2017). As various groups have been reportedly using Mozilla Common Voice to democratize speech technology for their mother tongues,11 we believe such community-driven projects can take the critical role of LPAs in establishing a written norm within the language community (Sackett, 2017; McCarty, 2018).

7 Conclusions

This study investigated the quality of prominent public speech datasets, highlighting the critical need for sociolinguistic awareness and the importance of language planning in dataset design, especially for less-institutionalized languages. We also proposed actionable guidelines for future speech dataset creation.

For downstream users of existing datasets, we strongly recommend human evaluation by native speakers or linguists, and data cleaning/filtering before use. Precise scoping of language/dialect variants is crucial to avoid mixed-language issues in training multilingual models. We also recommend employing more flexible evaluation metrics than WER and CER, such as those proposed by Nigmatulina et al. (2020) and Karita et al. (2023).

Future work can focus on developing practical tools and frameworks for implementing our proposed guidelines. We also recommend closely monitoring Common Voice’s ongoing expansion into new languages. Future research should also address the limitations of current language classification systems, e.g. the ISO 639-3 standard, in representing the full spectrum of linguistic diversity and nuanced sociolinguistic realities. More importantly, we recommend investigating how the process of community-driven ASR dataset construction can

9discourse.mozilla.org/t/merging-norwegian-nynorsk-and-norwegian-bokmal/130474
10github.com/common-voice/common-voice/issues/4454
11How this grassroots effort could make AI voices more diverse.
assist language revitalization and corpus planning efforts, particularly in the orthography development of less-institutionalized languages (Bianco, 2018).

8 Limitations

While this study covered over 40 languages, a significant number remain uninspected in the evaluated datasets. Future work should extend our investigation to these languages and develop practical tools and frameworks for implementing the proposed guidelines. Our proposed framework assumes access to linguistic expert and native speaker resource. Further research can focus on how small teams and community can overcome the lack of such resource and build sociolinguistically-informed datasets.

9 Ethical Considerations

There are no known ethical concerns or risks associated with this work.

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A  License or Terms for Use of Artifacts

In this paper we studied three datasets VoxPopuli, FLEURS and MCV17. VoxPopuli is under CC-BY-NC 4.0 license12, FLEURS is under CC-BY 4.0 license13 and MCV17 is under CCO 1.0 license14. In addition, we used canto-filter package in 4.2.2. The package is under MIT license15. The usage of each dataset and package in our work is consistent with its intended use according to the license.

B  Meta-Information of VoxPopuli, FLEURS and MCV17

The VoxPopuli corpus contains 1.8k hours of transcribed speech in 16 European languages, detailed in Table 6.

<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th>hours</th>
    <th>code</th>
    <th>name</th>
    <th>hours</th>
  </tr>
  <tr>
    <td>cs</td>
    <td>Czech</td>
    <td>62</td>
    <td>hu</td>
    <td>Hungarian</td>
    <td>63</td>
  </tr>
  <tr>
    <td>de</td>
    <td>German</td>
    <td>282</td>
    <td>it</td>
    <td>Italian</td>
    <td>91</td>
  </tr>
  <tr>
    <td>en</td>
    <td>English</td>
    <td>543</td>
    <td>lt</td>
    <td>Lithuanian</td>
    <td>2</td>
  </tr>
  <tr>
    <td>es</td>
    <td>Spanish</td>
    <td>166</td>
    <td>nl</td>
    <td>Dutch</td>
    <td>53</td>
  </tr>
  <tr>
    <td>et</td>
    <td>Estonian</td>
    <td>3</td>
    <td>pl</td>
    <td>Polish</td>
    <td>111</td>
  </tr>
  <tr>
    <td>fi</td>
    <td>Finnish</td>
    <td>27</td>
    <td>ro</td>
    <td>Romanian</td>
    <td>89</td>
  </tr>
  <tr>
    <td>fr</td>
    <td>French</td>
    <td>211</td>
    <td>sk</td>
    <td>Slovak</td>
    <td>35</td>
  </tr>
  <tr>
    <td>hr</td>
    <td>Croatian</td>
    <td>43</td>
    <td>sl</td>
    <td>Slovene</td>
    <td>10</td>
  </tr>
</table>

Table 6: List of languages and hours in VoxPopuli

FLEURS contains English utterances their translations and readings into 101 languages. Around 2009 English sentences are extracted from FLoRes101 corpus. Each sentence is recorded by 3 native speakers and the invalid recordings are discarded, which makes a total of 1.4k hours of speech and around 12 hours in each language. The full list of languages is shown in Table 7 and 8.

<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th>code</th>
    <th>name</th>
  </tr>
  <tr>
    <td>af_za</td>
    <td>Afrikaans, South Africa</td>
    <td>el_gr</td>
    <td>Greek, Greece</td>
  </tr>
  <tr>
    <td>am_et</td>
    <td>Amharic, Ethiopia</td>
    <td>en_us</td>
    <td>English, United States</td>
  </tr>
  <tr>
    <td>ar_eg</td>
    <td>Arabic, Egypt</td>
    <td>es_419</td>
    <td>Spanish, Latin America</td>
  </tr>
  <tr>
    <td>as_in</td>
    <td>Assamese, India</td>
    <td>et_ee</td>
    <td>Estonian, Estonia</td>
  </tr>
  <tr>
    <td>ast_es</td>
    <td>Asturian, Spain</td>
    <td>fa_ir</td>
    <td>Persian, Iran</td>
  </tr>
  <tr>
    <td>az_az</td>
    <td>Azerbaijani, Azerbaijan</td>
    <td>ff_sn</td>
    <td>Fulah, Senegal</td>
  </tr>
  <tr>
    <td>be_by</td>
    <td>Belarusian, Belarus</td>
    <td>fi_fi</td>
    <td>Finnish, Finland</td>
  </tr>
  <tr>
    <td>bg_bg</td>
    <td>Bulgarian, Bulgaria</td>
    <td>fil_ph</td>
    <td>Filipino, Philippines</td>
  </tr>
  <tr>
    <td>bn_in</td>
    <td>Bengali, India</td>
    <td>fr_fr</td>
    <td>French, France</td>
  </tr>
  <tr>
    <td>bs_ba</td>
    <td>Bosnian, Bosnia</td>
    <td>ga_ie</td>
    <td>Irish, Ireland</td>
  </tr>
  <tr>
    <td>ca_es</td>
    <td>Catalan, Spain</td>
    <td>gl_es</td>
    <td>Galician, Spain</td>
  </tr>
  <tr>
    <td>ceb_ph</td>
    <td>Cebuano, Philippines</td>
    <td>gu_in</td>
    <td>Gujarati, India</td>
  </tr>
  <tr>
    <td>ckb_iq</td>
    <td>Central Kurdish, Iraq</td>
    <td>ha_ng</td>
    <td>Hausa, Nigeria</td>
  </tr>
  <tr>
    <td>cmn_hans_cn</td>
    <td>Mandarin, China</td>
    <td>he_il</td>
    <td>Hebrew, Israel</td>
  </tr>
  <tr>
    <td>cs_cz</td>
    <td>Czech, Czech Republic</td>
    <td>hi_in</td>
    <td>Hindi, India</td>
  </tr>
  <tr>
    <td>cy_gb</td>
    <td>Welsh, United Kingdom</td>
    <td>hr_hr</td>
    <td>Croatian, Croatia</td>
  </tr>
  <tr>
    <td>da_dk</td>
    <td>Danish, Denmark</td>
    <td>hu_hu</td>
    <td>Hungarian, Hungary</td>
  </tr>
  <tr>
    <td>de_de</td>
    <td>German, Germany</td>
    <td>hy_am</td>
    <td>Armenian, Armenia</td>
  </tr>
</table>

Table 7: List of languages in FLEURS, Part 1

12 github.com/facebookresearch/voxpopuli/blob/main/LICENSE
13 huggingface.co/datasets/google/fleurs
14 huggingface.co/datasets/mozilla-foundation/common_voice_17_0
15 pypi.org/project/canto-filter/
<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th>code</th>
    <th>name</th>
  </tr>
  <tr>
    <td>id_id</td>
    <td>Indonesian, Indonesia</td>
    <td>ny_mw</td>
    <td>Chichewa, Malawi</td>
  </tr>
  <tr>
    <td>ig_ng</td>
    <td>Igbo, Nigeria</td>
    <td>oc_fr</td>
    <td>Occitan, France</td>
  </tr>
  <tr>
    <td>is_is</td>
    <td>Icelandic, Iceland</td>
    <td>om_et</td>
    <td>Oromo, Ethiopia</td>
  </tr>
  <tr>
    <td>it_it</td>
    <td>Italian, Italy</td>
    <td>or_in</td>
    <td>Odia, India</td>
  </tr>
  <tr>
    <td>ja_jp</td>
    <td>Japanese, Japan</td>
    <td>pa_in</td>
    <td>Punjabi, India</td>
  </tr>
  <tr>
    <td>jv_id</td>
    <td>Javanese, Indonesia</td>
    <td>pl_pl</td>
    <td>Polish, Poland</td>
  </tr>
  <tr>
    <td>ka_ge</td>
    <td>Georgian, Georgia</td>
    <td>ps_af</td>
    <td>Pashto, Afghanistan</td>
  </tr>
  <tr>
    <td>kam_ke</td>
    <td>Kamba, Kenya</td>
    <td>pt_br</td>
    <td>Portuguese, Brazil</td>
  </tr>
  <tr>
    <td>kea_cv</td>
    <td>Kabuverdianu, Cape Verde</td>
    <td>ro_ro</td>
    <td>Romanian, Romania</td>
  </tr>
  <tr>
    <td>kk_kz</td>
    <td>Kazakh, Kazakhstan</td>
    <td>ru_ru</td>
    <td>Russian, Russia</td>
  </tr>
  <tr>
    <td>km_kh</td>
    <td>Khmer, Cambodia</td>
    <td>sd_in</td>
    <td>Sindhi, India</td>
  </tr>
  <tr>
    <td>kn_in</td>
    <td>Kannada, India</td>
    <td>sk_sk</td>
    <td>Slovak, Slovakia</td>
  </tr>
  <tr>
    <td>ko_kr</td>
    <td>Korean, South Korea</td>
    <td>sl_si</td>
    <td>Slovenian, Slovenia</td>
  </tr>
  <tr>
    <td>ky_kg</td>
    <td>Kyrgyz, Kyrgyzstan</td>
    <td>sn_zw</td>
    <td>Shona, Zimbabwe</td>
  </tr>
  <tr>
    <td>lb_lu</td>
    <td>Luxembourgish, Luxembourg</td>
    <td>so_so</td>
    <td>Somali, Somalia</td>
  </tr>
  <tr>
    <td>lg_ug</td>
    <td>Ganda, Uganda</td>
    <td>sr_rs</td>
    <td>Serbian, Serbia</td>
  </tr>
  <tr>
    <td>ln_cd</td>
    <td>Lingala, DRC</td>
    <td>sv_se</td>
    <td>Swedish, Sweden</td>
  </tr>
  <tr>
    <td>lo_la</td>
    <td>Lao, Laos</td>
    <td>sw_ke</td>
    <td>Swahili, Kenya</td>
  </tr>
  <tr>
    <td>lt_lt</td>
    <td>Lithuanian, Lithuania</td>
    <td>ta_in</td>
    <td>Tamil, India</td>
  </tr>
  <tr>
    <td>luo_ke</td>
    <td>Luo, Kenya</td>
    <td>te_in</td>
    <td>Telugu, India</td>
  </tr>
  <tr>
    <td>lv_lv</td>
    <td>Latvian, Latvia</td>
    <td>tg_tj</td>
    <td>Tajik, Tajikistan</td>
  </tr>
  <tr>
    <td>mi_nz</td>
    <td>Māori, New Zealand</td>
    <td>th_th</td>
    <td>Thai, Thailand</td>
  </tr>
  <tr>
    <td>mk_mk</td>
    <td>Macedonian, North Macedonia</td>
    <td>tr_tr</td>
    <td>Turkish, Turkey</td>
  </tr>
  <tr>
    <td>ml_in</td>
    <td>Malayalam, India</td>
    <td>uk_ua</td>
    <td>Ukrainian, Ukraine</td>
  </tr>
  <tr>
    <td>mn_mn</td>
    <td>Mongolian, Mongolia</td>
    <td>umb_ao</td>
    <td>Umbundu, Angola</td>
  </tr>
  <tr>
    <td>mr_in</td>
    <td>Marathi, India</td>
    <td>ur_pk</td>
    <td>Urdu, Pakistan</td>
  </tr>
  <tr>
    <td>ms_my</td>
    <td>Malay, Malaysia</td>
    <td>uz_uz</td>
    <td>Uzbek, Uzbekistan</td>
  </tr>
  <tr>
    <td>mt_mt</td>
    <td>Maltese, Malta</td>
    <td>vi_vn</td>
    <td>Vietnamese, Vietnam</td>
  </tr>
  <tr>
    <td>my_mm</td>
    <td>Burmese, Myanmar</td>
    <td>wo_sn</td>
    <td>Wolof, Senegal</td>
  </tr>
  <tr>
    <td>nb_no</td>
    <td>Norwegian Bokmål, Norway</td>
    <td>xh_za</td>
    <td>Xhosa, South Africa</td>
  </tr>
  <tr>
    <td>ne_np</td>
    <td>Nepali, Nepal</td>
    <td>yo_ng</td>
    <td>Yoruba, Nigeria</td>
  </tr>
  <tr>
    <td>nl_nl</td>
    <td>Dutch, Netherlands</td>
    <td>yue_hant_hk</td>
    <td>Cantonese, Hong Kong</td>
  </tr>
  <tr>
    <td>nso_za</td>
    <td>Northern Sotho, South Africa</td>
    <td>zu_za</td>
    <td>Zulu, South Africa</td>
  </tr>
</table>

Table 8: List of languages in FLEURS, Part 2
The statistics of MCV17 meta-information can be found in Table 9 and 10. The full dataset contains 124 locales. Each locale contains 3 categories: validated, invalidated and other. Validated means the data clip has received more than 2 validations and the upvotes > downvotes; Invalidated means the data clip has received more than 2 validations but the upvotes \( \leq \) downvotes; Other means the data clip has not received 2 or more validations.

When analyzing the speaker diversity in Section 3.4, we used all the 3 categories. In other analyses, we only used the validated data and skipped locales with 0 or 1 utterances.

<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th colspan="2">total hours</th>
    <th colspan="2">validated hours</th>
    <th>code</th>
    <th>name</th>
    <th colspan="2">total hours</th>
    <th colspan="2">validated hours</th>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>hours</td>
    <td></td>
    <td>hours</td>
    <td></td>
    <td></td>
    <td></td>
    <td>hours</td>
    <td></td>
    <td>hours</td>
    <td></td>
  </tr>
  <tr><td>ab</td><td>Abkhaz</td><td>84.41</td><td></td><td>59.86</td><td></td><td>es</td><td>Spanish</td><td>2219.39</td><td></td><td>561.82</td><td></td></tr>
  <tr><td>af</td><td>Afrikaans</td><td>0.56</td><td></td><td>0.27</td><td></td><td>et</td><td>Estonian</td><td>59.97</td><td></td><td>45.85</td><td></td></tr>
  <tr><td>am</td><td>Amharic</td><td>2.63</td><td></td><td>1.55</td><td></td><td>eu</td><td>Basque</td><td>672.41</td><td></td><td>273.48</td><td></td></tr>
  <tr><td>ar</td><td>Arabic</td><td>155.81</td><td></td><td>90.27</td><td></td><td>fa</td><td>Persian</td><td>415.36</td><td></td><td>363.42</td><td></td></tr>
  <tr><td>as</td><td>Assamese</td><td>3.28</td><td></td><td>2.72</td><td></td><td>fi</td><td>Finnish</td><td>21.65</td><td></td><td>13.35</td><td></td></tr>
  <tr><td>ast</td><td>Asturian</td><td>1.91</td><td></td><td>0.81</td><td></td><td>fr</td><td>French</td><td>1147.41</td><td></td><td>1013.03</td><td></td></tr>
  <tr><td>az</td><td>Azerbaijani</td><td>0.45</td><td></td><td>0.19</td><td></td><td>fy_nl</td><td>West Frisian</td><td>212.09</td><td></td><td>68.73</td><td></td></tr>
  <tr><td>ba</td><td>Bashkir</td><td>268.17</td><td></td><td>257.77</td><td></td><td>ga_ie</td><td>Irish</td><td>10.47</td><td></td><td>5.48</td><td></td></tr>
  <tr><td>bas</td><td>Basaa</td><td>2.82</td><td></td><td>2.16</td><td></td><td>gl</td><td>Galician</td><td>131.47</td><td></td><td>65.57</td><td></td></tr>
  <tr><td>be</td><td>Belarusian</td><td>1765.25</td><td></td><td>1717.14</td><td></td><td>gn</td><td>Guaraní</td><td>27.56</td><td></td><td>3.66</td><td></td></tr>
  <tr><td>bg</td><td>Bulgarian</td><td>20.77</td><td></td><td>16.46</td><td></td><td>ha</td><td>Hausa</td><td>12.17</td><td></td><td>3.94</td><td></td></tr>
  <tr><td>bn</td><td>Bengali</td><td>1272.95</td><td></td><td>53.51</td><td></td><td>he</td><td>Hebrew</td><td>6.06</td><td></td><td>2.22</td><td></td></tr>
  <tr><td>br</td><td>Breton</td><td>26.87</td><td></td><td>18.55</td><td></td><td>hi</td><td>Hindi</td><td>20.7</td><td></td><td>14.11</td><td></td></tr>
  <tr><td>ca</td><td>Catalan</td><td>3586.54</td><td></td><td>2697.77</td><td></td><td>hsb</td><td>Upper Sorbian</td><td>3.02</td><td></td><td>2.43</td><td></td></tr>
  <tr><td>ckb</td><td>Central Kurdish</td><td>172.16</td><td></td><td>130.21</td><td></td><td>ht</td><td>Haitian Creole</td><td>0.01</td><td></td><td>0</td><td></td></tr>
  <tr><td>cnh</td><td>Chin Haka</td><td>6.04</td><td></td><td>2.4</td><td></td><td>hu</td><td>Hungarian</td><td>172.63</td><td></td><td>92.64</td><td></td></tr>
  <tr><td>cs</td><td>Czech</td><td>262.67</td><td></td><td>76.09</td><td></td><td>hy_am</td><td>Armenian</td><td>47.36</td><td></td><td>22.27</td><td></td></tr>
  <tr><td>cv</td><td>Chuvash</td><td>27.51</td><td></td><td>24.36</td><td></td><td>ia</td><td>Interlingua</td><td>17.04</td><td></td><td>13.73</td><td></td></tr>
  <tr><td>cy</td><td>Welsh</td><td>156.58</td><td></td><td>123.08</td><td></td><td>id</td><td>Indonesian</td><td>64.53</td><td></td><td>28.93</td><td></td></tr>
  <tr><td>da</td><td>Danish</td><td>12.6</td><td></td><td>11.68</td><td></td><td>ig</td><td>Igbo</td><td>8.77</td><td></td><td>0.02</td><td></td></tr>
  <tr><td>de</td><td>German</td><td>1423.9</td><td></td><td>1333.93</td><td></td><td>is</td><td>Icelandic</td><td>0.08</td><td></td><td>0.02</td><td></td></tr>
  <tr><td>dv</td><td>Dhivehi</td><td>64.21</td><td></td><td>38.87</td><td></td><td>it</td><td>Italian</td><td>395.57</td><td></td><td>354.96</td><td></td></tr>
  <tr><td>dyu</td><td>Dyula</td><td>0.49</td><td></td><td>0.32</td><td></td><td>ja</td><td>Japanese</td><td>476.98</td><td></td><td>124.31</td><td></td></tr>
  <tr><td>el</td><td>Greek</td><td>31.5</td><td></td><td>18.64</td><td></td><td>ka</td><td>Georgian</td><td>214.21</td><td></td><td>138.8</td><td></td></tr>
  <tr><td>en</td><td>English</td><td>3507.22</td><td></td><td>2614.76</td><td></td><td>kab</td><td>Kabyle</td><td>689.77</td><td></td><td>566.48</td><td></td></tr>
  <tr><td>eo</td><td>Esperanto</td><td>1904.97</td><td></td><td>1433.1</td><td></td><td>kk</td><td>Kazakh</td><td>3.46</td><td></td><td>2.13</td><td></td></tr>
  <tr><td>es</td><td>Spanish</td><td>2219.39</td><td></td><td>561.82</td><td></td><td>kmr</td><td>Kurmanji Kurdish</td><td>99.79</td><td></td><td>67.57</td><td></td></tr>
  <tr><td>et</td><td>Estonian</td><td>59.97</td><td></td><td>45.85</td><td></td><td>ko</td><td>Korean</td><td>5.57</td><td></td><td>1.72</td><td></td></tr>
</table>

Table 9: List of locales and hours in Common Voice 17.0, Part 1
<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th>total hours</th>
    <th>validated hours</th>
    <th>code</th>
    <th>name</th>
    <th>total hours</th>
    <th>validated hours</th>
  </tr>
  <tr><td>ky</td><td>Kyrgyz</td><td>47.6</td><td>38.42</td><td>sah</td><td>Sakha</td><td>12.65</td><td>8.31</td></tr>
  <tr><td>lg</td><td>Ganda</td><td>559.22</td><td>436.7</td><td>sat</td><td>Santali</td><td>1.02</td><td>0.57</td></tr>
  <tr><td>lij</td><td>Ligurian</td><td>3.29</td><td>2.8</td><td>sc</td><td>Sardinian</td><td>1.95</td><td>1.5</td></tr>
  <tr><td>lo</td><td>Lao</td><td>0.37</td><td>0.2</td><td>sk</td><td>Slovak</td><td>26.89</td><td>22.1</td></tr>
  <tr><td>lt</td><td>Lithuanian</td><td>25.21</td><td>23.71</td><td>skr</td><td>Saraiki</td><td>6.59</td><td>4.2</td></tr>
  <tr><td>ltg</td><td>Latgalian</td><td>23.3</td><td>20.67</td><td>sl</td><td>Slovenian</td><td>14.99</td><td>11.38</td></tr>
  <tr><td>lv</td><td>Latvian</td><td>276.61</td><td>222.45</td><td>sq</td><td>Albanian</td><td>1.97</td><td>1.94</td></tr>
  <tr><td>mdf</td><td>Moksha</td><td>0.5</td><td>0.49</td><td>sr</td><td>Serbian</td><td>6.76</td><td>5.01</td></tr>
  <tr><td>mhr</td><td>Meadow Mari</td><td>301.25</td><td>280.45</td><td>sv_se</td><td>Swedish</td><td>54.5</td><td>45.38</td></tr>
  <tr><td>mk</td><td>Macedonian</td><td>22.89</td><td>7.82</td><td>sw</td><td>Swahili</td><td>1084.72</td><td>399.48</td></tr>
  <tr><td>ml</td><td>Malayalam</td><td>10.11</td><td>3.46</td><td>ta</td><td>Tamil</td><td>403.84</td><td>232.59</td></tr>
  <tr><td>mn</td><td>Mongolian</td><td>23.08</td><td>13.17</td><td>te</td><td>Telugu</td><td>2.3</td><td>0.26</td></tr>
  <tr><td>mr</td><td>Marathi</td><td>27.48</td><td>18.75</td><td>th</td><td>Thai</td><td>422.96</td><td>171.29</td></tr>
  <tr><td>mr_j</td><td>Western Mari</td><td>36.77</td><td>33.63</td><td>ti</td><td>Tigrinya</td><td>0.11</td><td>0.03</td></tr>
  <tr><td>mt</td><td>Maltese</td><td>17.21</td><td>8.48</td><td>tig</td><td>Tigre</td><td>2.69</td><td>1.08</td></tr>
  <tr><td>myv</td><td>Erzya</td><td>3.2</td><td>3.15</td><td>tk</td><td>Turkmen</td><td>6.52</td><td>2.73</td></tr>
  <tr><td>nan_tw</td><td>Southern Min (Taiwan)</td><td>20.21</td><td>5.73</td><td>tok</td><td>Toki Pona</td><td>18.86</td><td>13.59</td></tr>
  <tr><td>ne_np</td><td>Nepali</td><td>1.54</td><td>0.81</td><td>tr</td><td>Turkish</td><td>122.06</td><td>117.28</td></tr>
  <tr><td>nhi</td><td>Nahuatl</td><td>0.03</td><td>0.02</td><td>tt</td><td>Tatar</td><td>31.24</td><td>30.59</td></tr>
  <tr><td>nl</td><td>Dutch</td><td>119.6</td><td>109.48</td><td>tw</td><td>Twi</td><td>0.27</td><td>0.16</td></tr>
  <tr><td>nn_no</td><td>Norwegian Nynorsk</td><td>1.67</td><td>1.42</td><td>ug</td><td>Uyghur</td><td>236.63</td><td>199.46</td></tr>
  <tr><td>nso</td><td>Northern Sotho</td><td>0.03</td><td>0</td><td>uk</td><td>Ukrainian</td><td>111.71</td><td>97.44</td></tr>
  <tr><td>oc</td><td>Occitan</td><td>12.83</td><td>2.25</td><td>ur</td><td>Urdu</td><td>231.8</td><td>63.52</td></tr>
  <tr><td>or</td><td>Odia</td><td>12.51</td><td>4.4</td><td>uz</td><td>Uzbek</td><td>263.24</td><td>99.63</td></tr>
  <tr><td>os</td><td>Ossetic</td><td>0.31</td><td>0.28</td><td>vi</td><td>Vietnamese</td><td>18.76</td><td>5.65</td></tr>
  <tr><td>pa_in</td><td>Punjabi</td><td>3.99</td><td>2.01</td><td>vot</td><td>Votic</td><td>0.29</td><td>0.06</td></tr>
  <tr><td>pl</td><td>Polish</td><td>175.85</td><td>166.71</td><td>yi</td><td>Yiddish</td><td>0.05</td><td>0.04</td></tr>
  <tr><td>ps</td><td>Pashto</td><td>2.11</td><td>1.69</td><td>yo</td><td>Yoruba</td><td>7.29</td><td>5.07</td></tr>
  <tr><td>pt</td><td>Portuguese</td><td>210.97</td><td>174.17</td><td>yue</td><td>Cantonese</td><td>177.76</td><td>23.4</td></tr>
  <tr><td>quy</td><td>Quechua (Ayacucho)</td><td>0.01</td><td>0</td><td>zgh</td><td>Tamazight</td><td>1.47</td><td>0.51</td></tr>
  <tr><td>rm_sursilv</td><td>Romansh Sursilvan</td><td>10.91</td><td>6.53</td><td>zh_cn</td><td>Chinese (China)</td><td>1061.32</td><td>233.77</td></tr>
  <tr><td>rm_vallader</td><td>Romansh Vallader</td><td>4.26</td><td>2.46</td><td>zh_hk</td><td>Chinese (Hong Kong)</td><td>138.23</td><td>107.42</td></tr>
  <tr><td>ro</td><td>Romanian</td><td>46.8</td><td>19.85</td><td>zh_tw</td><td>Chinese (Taiwan)</td><td>126.04</td><td>77.1</td></tr>
  <tr><td>ru</td><td-Russian></td><td>273.89</td><td>234.46</td><td>zu</td><td>Zulu</td><td>0.05</td><td>0</td></tr>
  <tr><td>rw</td><td>Kinyarwanda</td><td>2384</td><td>2001.34</td><td>zza</td><td>Zaza</td><td>0.45</td><td>0.33</td></tr>
</table>

Table 10: List of locales and hours in Common Voice 17.0, Part 2
C  Median Utterance Duration of VoxPopuli, FLEURS and MCV17

![Bar chart showing median utterance duration for 14 languages in VoxPopuli](page_232_186_1089_377.png)

Figure 1: Median utterance duration of the 14 languages in VoxPopuli. All languages have a median utterance duration of at least 7 seconds.

![Bar chart showing median utterance duration for 50 languages in FLEURS](page_232_624_1089_377.png)

Figure 2: 50 languages with the shortest median utterance duration in FLEURS. All languages have a median utterance duration of at least 5 seconds.
Figure 3: 60 languages with the shortest median utterance duration in Mozilla Common Voice 17.0. Taiwanese Southern Min (nan_tw) has a median duration of only 2.45 seconds.

Figure 4: 60 languages with the shortest median text prompt length (measured by word or character count) in Mozilla Common Voice 17.0. Note that nan_tw doesn’t have the shortest length because the words are duplicated in two writing systems.
D  Speech and Silence Percentage of VoxPopuli, FLEURS and MCV17

![Bar chart showing percentages of speech and silence for various languages in VoxPopuli](page_246_312_1057_482.png)

Figure 5: Percentages of speech and silence in VoxPopuli. All languages have more than 90% speech in the utterances. Note that the percentages of silence and speech don’t add up to 100% because we omit other types of sound such as music, noise, laughter, etc.
Figure 6: 50 languages with the lowest speech proportion in FLEURS. Danish (da_dk) has less than 50% speech in the audio recordings. Note that the percentages of silence and speech don’t add up to 100% because we omit other types of sound such as music, noise, laughter, etc.

Figure 7: 60 languages with the lowest speech proportion in Mozilla Common Voice 17.0. Basaa, Zaza, Serbian and Taiwanese Southern Min have less than 50% speech in the audio recordings. Note that the percentages of silence and speech don’t add up to 100% because we omit other types of sound such as music, noise, laughter, etc.
E Example Sentences of MCV17

<table>
  <tr>
    <th>zh_cn (Chinese, China)</th>
    <th>ca (Catalan)</th>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百一十一名。</td>
    <td>No he anat mai a Agost.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百七十一名。</td>
    <td>No he anat mai a Aigües.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百三十一名。</td>
    <td>No he anat mai a Aiora.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百三十九名。</td>
    <td>No he anat mai a Aiòder.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百三十五名。</td>
    <td>No he anat mai a Alaïor.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百九十名。</td>
    <td>No he anat mai a Alaró.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百五十名。</td>
    <td>No he anat mai a Alaïor.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百八十七名。</td>
    <td>No he anat mai a Albaida.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百八十九名。</td>
    <td>No he anat mai a Albatera.</td>
  </tr>
  <tr>
    <td>殿试登进士第三甲第一百六十七名。</td>
    <td>No he anat mai a Alberic.</td>
  </tr>
  <tr>
    <td>...</td>
    <td>...</td>
  </tr>
</table>

Table 11: Examples of highly repetitive, seemingly machine-generated sentences in Common Voice 17.0. We find at least hundreds of such sentences in zh_cn, zh_tw, zh_hk and ca. We suspect that similar issues may exist in other languages of MCV17 that we haven’t inspected.

<table>
  <tr>
    <th>nan_tw sentences in MCV17</th>
  </tr>
  <tr>
    <td>竹仔籬（Tik-á-nâ）</td>
  </tr>
  <tr>
    <td>竹南鎮（Tek-lâm-tìn）</td>
  </tr>
  <tr>
    <td>竹南鎮（Tik-lâm-tìn）</td>
  </tr>
  <tr>
    <td>竹坑口（Tik-khinn-kháu | Tek-khi-kháu）</td>
  </tr>
  <tr>
    <td>竹子腳（Tek-á-kha）</td>
  </tr>
  <tr>
    <td>竹崎鄉（Tik-kiā-hiong）</td>
  </tr>
  <tr>
    <td>竹東鎮（Tik-tang-tìn）</td>
  </tr>
  <tr>
    <td>竹東（Tik-tang）</td>
  </tr>
  <tr>
    <td>竹田鄉（Tik-tshân-hiong）</td>
  </tr>
  <tr>
    <td>竹田（Tik-tshân）</td>
  </tr>
  <tr>
    <td>...</td>
  </tr>
</table>

Table 12: A snapshot of MCV17 nan_tw’s text prompts. Each row is a single word or phrase with its romanization. For homographs, multiple pronunciations are appended to the Sinographs.
F  Average Hours per Speaker of MCV17

<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th>unique speakers</th>
    <th>total hours</th>
    <th>average hours per speaker</th>
  </tr>
  <tr><td>rw</td><td>Kinyarwanda</td><td>1131</td><td>2,384.0</td><td>2.11</td></tr>
  <tr><td>mk</td><td>Macedonian</td><td>19</td><td>22.9</td><td>1.20</td></tr>
  <tr><td>eo</td><td>Esperanto</td><td>1739</td><td>1,905.0</td><td>1.10</td></tr>
  <tr><td>lg</td><td>Ganda</td><td>657</td><td>559.2</td><td>0.85</td></tr>
  <tr><td>sw</td><td>Swahili</td><td>1452</td><td>1,084.7</td><td>0.75</td></tr>
  <tr><td>ur</td><td>Urdu</td><td>349</td><td>231.8</td><td>0.66</td></tr>
  <tr><td>mr_j</td><td>Western Mari</td><td>60</td><td>36.8</td><td>0.61</td></tr>
  <tr><td>mhr</td><td>Meadow Mari</td><td>496</td><td>301.3</td><td>0.61</td></tr>
  <tr><td>kab</td><td>Kabyle</td><td>1547</td><td>689.8</td><td>0.45</td></tr>
  <tr><td>ta</td><td>Tamil</td><td>906</td><td>403.8</td><td>0.45</td></tr>
  <tr><td>mr</td><td>Marathi</td><td>90</td><td>27.5</td><td>0.31</td></tr>
  <tr><td>ha</td><td>Hausa</td><td>40</td><td>12.2</td><td>0.30</td></tr>
  <tr><td>ba</td><td>Bashkir</td><td>917</td><td>268.2</td><td>0.29</td></tr>
  <tr><td>he</td><td>Hebrew</td><td>21</td><td>6.1</td><td>0.29</td></tr>
  <tr><td>cs</td><td>Czech</td><td>983</td><td>262.7</td><td>0.27</td></tr>
  <tr><td>ia</td><td>Interlingua</td><td>67</td><td>17.0</td><td>0.25</td></tr>
  <tr><td>myv</td><td>Erzya</td><td>13</td><td>3.2</td><td>0.25</td></tr>
  <tr><td>cv</td><td>Chuvash</td><td>112</td><td>27.5</td><td>0.25</td></tr>
  <tr><td>ps</td><td>Pashto</td><td>9</td><td>2.1</td><td>0.23</td></tr>
  <tr><td>be</td><td>Belarusian</td><td>8291</td><td>1,765.25</td><td>0.21</td></tr>
  <tr><td>ab</td><td>Abkhaz</td><td>403</td><td>84.41</td><td>0.21</td></tr>
  <tr><td>yue</td><td>Cantonese</td><td>913</td><td>177.76</td><td>0.19</td></tr>
  <tr><td>ug</td><td>Uyghur</td><td>1258</td><td>236.63</td><td>0.19</td></tr>
  <tr><td>dv</td><td>Dhivehi</td><td>357</td><td>64.21</td><td>0.18</td></tr>
  <tr><td>kmr</td><td>Kurmanji Kurdish</td><td>561</td><td>99.79</td><td>0.18</td></tr>
  <tr><td>lij</td><td>Ligurian</td><td>19</td><td>3.29</td><td>0.17</td></tr>
  <tr><td>ky</td><td>Kyrgyz</td><td>283</td><td>47.60</td><td>0.17</td></tr>
  <tr><td>gn</td><td>Guarani</td><td>164</td><td>27.56</td><td>0.17</td></tr>
  <tr><td>bg</td><td>Bulgarian</td><td>134</td><td>20.77</td><td>0.15</td></tr>
  <tr><td>zh_cn</td><td>Chinese (China)</td><td>7005</td><td>1,061.32</td><td>0.15</td></tr>
  <tr><td>hsb</td><td>Upper Sorbian</td><td>21</td><td>3.02</td><td>0.14</td></tr>
  <tr><td>sc</td><td>Sardinian</td><td>14</td><td>1.95</td><td>0.14</td></tr>
  <tr><td>br</td><td>Breton</td><td>207</td><td>26.87</td><td>0.13</td></tr>
  <tr><td>ka</td><td>Georgian</td><td>1679</td><td>214.21</td><td>0.13</td></tr>
  <tr><td>tok</td><td>Toki Pona</td><td>149</td><td>18.86</td><td>0.13</td></tr>
  <tr><td>hy_an</td><td>Armenian</td><td>390</td><td>47.36</td><td>0.12</td></tr>
  <tr><td>uz</td><td>Uzbek</td><td>2170</td><td>263.24</td><td>0.12</td></tr>
  <tr><td>rm_sursilv</td><td>Romansh Sursilvan</td><td>90</td><td>10.91</td><td>0.12</td></tr>
  <tr><td>tt</td><td>Tatar</td><td>258</td><td>31.24</td><td>0.12</td></tr>
</table>

Table 13: Top 40 languages with the highest average total hours per speaker in Common Voice 17.0. As a reference, English has 0.04 hours per speaker.
<table>
  <tr>
    <th>code</th>
    <th>name</th>
    <th>unique speakers</th>
    <th>total hours</th>
    <th>average hours per speaker</th>
    <th>code</th>
    <th>name</th>
    <th>unique speakers</th>
    <th>total hours</th>
    <th>average hours per speaker</th>
  </tr>
  <tr>
    <td>zu</td>
    <td>Zulu</td>
    <td>1</td>
    <td>0.05</td>
    <td>0.05</td>
    <td>bas</td>
    <td>Basaa</td>
    <td>36</td>
    <td>2.82</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>nso</td>
    <td>Northern Sotho</td>
    <td>1</td>
    <td>0.03</td>
    <td>0.03</td>
    <td>nn_no</td>
    <td>Norwegian Nynorsk</td>
    <td>38</td>
    <td>1.67</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>ht</td>
    <td>Haitian Creole</td>
    <td>1</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>te</td>
    <td>Telugu</td>
    <td>39</td>
    <td>2.30</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>nhi</td>
    <td>Nahuatl</td>
    <td>2</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>ha</td>
    <td>Hausa</td>
    <td>40</td>
    <td>12.17</td>
    <td>0.30</td>
  </tr>
  <tr>
    <td>quy</td>
    <td>Quechua (Ayacucho)</td>
    <td>2</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>as</td>
    <td>Assamese</td>
    <td>46</td>
    <td>3.28</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>yi</td>
    <td>Yiddish</td>
    <td>3</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>rm_vallader</td>
    <td>Romansh Vallader</td>
    <td>53</td>
    <td>4.26</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>zza</td>
    <td>Zaza</td>
    <td>4</td>
    <td>0.45</td>
    <td>0.11</td>
    <td>sq</td>
    <td>Albanian</td>
    <td>55</td>
    <td>1.97</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>is</td>
    <td>Icelandic</td>
    <td>4</td>
    <td>0.08</td>
    <td>0.02</td>
    <td>skr</td>
    <td>Saraiki</td>
    <td>57</td>
    <td>6.59</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>vot</td>
    <td>Votic</td>
    <td>6</td>
    <td>0.29</td>
    <td>0.05</td>
    <td>mrj</td>
    <td>Western Mari</td>
    <td>60</td>
    <td>36.77</td>
    <td>0.61</td>
  </tr>
  <tr>
    <td>tw</td>
    <td>Twi</td>
    <td>6</td>
    <td>0.27</td>
    <td>0.05</td>
    <td>ia</td>
    <td>Interlingua</td>
    <td>67</td>
    <td>17.04</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>ti</td>
    <td>Tigrinya</td>
    <td>6</td>
    <td>0.11</td>
    <td>0.02</td>
    <td>pa_in</td>
    <td>Punjabi</td>
    <td>68</td>
    <td>3.99</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>os</td>
    <td>Ossetic</td>
    <td>8</td>
    <td>0.31</td>
    <td>0.04</td>
    <td>mr</td>
    <td>Marathi</td>
    <td>90</td>
    <td>27.48</td>
    <td>0.31</td>
  </tr>
  <tr>
    <td>ps</td>
    <td>Pashto</td>
    <td>9</td>
    <td>2.11</td>
    <td>0.23</td>
    <td>rm_sursilv</td>
    <td>Romansh Sursilvan</td>
    <td>90</td>
    <td>10.91</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>mdf</td>
    <td>Moksha</td>
    <td>11</td>
    <td>0.50</td>
    <td>0.05</td>
    <td>ko</td>
    <td>Korean</td>
    <td>90</td>
    <td>5.57</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>myv</td>
    <td>Erzya</td>
    <td>13</td>
    <td>3.20</td>
    <td>0.25</td>
    <td>yo</td>
    <td>Yoruba</td>
    <td>108</td>
    <td>7.29</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>sat</td>
    <td>Santali</td>
    <td>13</td>
    <td>1.02</td>
    <td>0.08</td>
    <td>sah</td>
    <td>Sakha</td>
    <td>111</td>
    <td>12.65</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>lo</td>
    <td>Lao</td>
    <td>13</td>
    <td>0.37</td>
    <td>0.03</td>
    <td>cv</td>
    <td>Chuvash</td>
    <td>112</td>
    <td>27.51</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>sc</td>
    <td>Sardinian</td>
    <td>14</td>
    <td>1.95</td>
    <td>0.14</td>
    <td>tk</td>
    <td>Turkmen</td>
    <td>112</td>
    <td>6.52</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>zgh</td>
    <td>Tamazight</td>
    <td>17</td>
    <td>1.47</td>
    <td>0.09</td>
    <td>ig</td>
    <td>Igbo</td>
    <td>114</td>
    <td>8.77</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>mk</td>
    <td>Macedonian</td>
    <td>19</td>
    <td>22.89</td>
    <td>1.20</td>
    <td>or</td>
    <td>Odia</td>
    <td>125</td>
    <td>12.51</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>lij</td>
    <td>Ligurian</td>
    <td>19</td>
    <td>3.29</td>
    <td>0.17</td>
    <td>bg</td>
    <td>Bulgarian</td>
    <td>134</td>
    <td>20.77</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>he</td>
    <td>Hebrew</td>
    <td>21</td>
    <td>6.06</td>
    <td>0.29</td>
    <td>ml</td>
    <td>Malayalam</td>
    <td>134</td>
    <td>10.11</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>hsb</td>
    <td>Upper Sorbian</td>
    <td>21</td>
    <td>3.02</td>
    <td>0.14</td>
    <td>oc</td>
    <td>Occitan</td>
    <td>145</td>
    <td>12.83</td>
    <td>0.09</td>
  </tr>
  <tr>
    <td>af</td>
    <td>Afrikaans</td>
    <td>23</td>
    <td>0.56</td>
    <td>0.02</td>
    <td>tok</td>
    <td>Toki Pona</td>
    <td>149</td>
    <td>18.86</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>tig</td>
    <td>Tigre</td>
    <td>24</td>
    <td>2.69</td>
    <td>0.11</td>
    <td>sr</td>
    <td>Serbian</td>
    <td>153</td>
    <td>6.76</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>az</td>
    <td>Azerbaijani</td>
    <td>26</td>
    <td>0.45</td>
    <td>0.02</td>
    <td>sl</td>
    <td>Slovenian</td>
    <td>154</td>
    <td>14.99</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>ast</td>
    <td>Asturian</td>
    <td>29</td>
    <td>1.91</td>
    <td>0.07</td>
    <td>gn</td>
    <td>Guarani</td>
    <td>164</td>
    <td>27.56</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>am</td>
    <td>Amharic</td>
    <td>30</td>
    <td>2.63</td>
    <td>0.09</td>
    <td>kk</td>
    <td>Kazakh</td>
    <td>166</td>
    <td>3.46</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>ne_np</td>
    <td>Nepali</td>
    <td>32</td>
    <td>1.54</td>
    <td>0.05</td>
    <td>ga_ie</td>
    <td>Irish</td>
    <td>192</td>
    <td>10.47</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>dyu</td>
    <td>Dyula</td>
    <td>33</td>
    <td>0.49</td>
    <td>0.01</td>
    <td>br</td>
    <td>Breton</td>
    <td>207</td>
    <td>26.87</td>
    <td>0.13</td>
  </tr>
</table>

Table 14: Bottom 60 languages with the least unique voice contributors in Common Voice 17.0. As a reference, English has 92325 speakers, with each contributing 0.04 hours on average.
G  Script Choices of Digraphic Languages in MCV17 and FLEURS

<table>
  <tr>
    <th>Language name</th>
    <th>Scripts that can be written in</th>
    <th>MCV17</th>
    <th>FLEURS</th>
  </tr>
  <tr>
    <td>Central Kurdish (Sorani)</td>
    <td>Cyrillic, Hawar (Latin), Sorani (Arabic)</td>
    <td>ckb: Arabic</td>
    <td>ckb_iq: Sorani (Arabic)</td>
  </tr>
  <tr>
    <td>Dyula</td>
    <td>Latin, N’Ko</td>
    <td>dyu: Latin</td>
    <td>N/A</td>
  </tr>
  <tr>
    <td>Fula</td>
    <td>Adlam, Ajami (Arabic), Latin</td>
    <td>N/A</td>
    <td>ff_sn: Latin</td>
  </tr>
  <tr>
    <td>Malay</td>
    <td>Jawi (Arabic), Latin</td>
    <td>ms: Latin</td>
    <td>ms_my: Latin</td>
  </tr>
  <tr>
    <td>Mongolian</td>
    <td>Cyrillic, Mongolian (Bichig)</td>
    <td>mn: Cyrillic</td>
    <td>mn_mn: Cyrillic</td>
  </tr>
  <tr>
    <td>Northern Kurdish (Kurmanji)</td>
    <td>Cyrillic, Hawar (Latin), Sorani (Arabic)</td>
    <td>kmr: Hawar (Latin)</td>
    <td>N/A</td>
  </tr>
  <tr>
    <td>Serbian</td>
    <td>Cyrillic, Latin</td>
    <td>sr: Cyrillic</td>
    <td>sr_rs: Cyrillic and Latin</td>
  </tr>
  <tr>
    <td>Punjabi</td>
    <td>Gurmukhi, Shahmukhi (Arabic)</td>
    <td>pa_in: Gurmukhi</td>
    <td>pa_in: Gurmukhi</td>
  </tr>
  <tr>
    <td>Tamazight (Berber a.k.a. Amazigh)</td>
    <td>Arabic, Latin, Tifinagh</td>
    <td>zgh: Tifinagh</td>
    <td>N/A</td>
  </tr>
  <tr>
    <td>Uzbek</td>
    <td>Arabic, Cyrillic, Latin</td>
    <td>uz: Latin</td>
    <td>uz_uz: Latin</td>
  </tr>
  <tr>
    <td>Votic</td>
    <td>Cyrillic, Latin</td>
    <td>vot: Latin</td>
    <td>N/A</td>
  </tr>
</table>

Table 15: Script choices and assumptions of digraphic / trigraphic languages in Common Voice 17.0 and FLEURS.

H  Example of Mixed-script Writings in Taiwanese Southern Min

逐日練習寫台文 第一百七十九工
你拚命leh衝，毋過嘛ài ê-ki-chit看顧家己。
Chóng--si曆日拆到最後卅張--ah，我chai-iá"你學校ê期尾考、公司ê業績、申請ê計畫等等，攞需要佢這個時間做一個收煞，所以你下心神leh chông。
你辛苦--ah，毋通kà家己操勞kah phah-phái"身體。
佢遮chhoân五種語言ê感謝予--lí，希望你ê-tàng得著濟濟溫暖。咱，鬥陣行過難關。

Figure 8: A Threads post written in Taiwanese Southern Min. Sinographs and Latin letters are used interchangeably without a codified convention.
1 Text Classification Logic

Algorithm 1 Norwegian Orthography Classification Logic

procedure CLASSIFYBOKMALNYNORSK(sentence)
    nynorskMarkers ← “ikkje”, “eg”, “eit”, “eitt”, “me”, “ho”, “hjå”, “kva”, “kven”, “noko”, “nokre”, “sjå”, “skule”, “kor”, “fyrst”, “mykje”, “ög”, “medan”
    bokmaalMarkers ← “ikke”, “jeg”, “et”, “en”, “vi”, “hun”, “hos”, “hva”, “hvem”, “noe”, “noen”, “se”, “skole”, “hvor”, “først”, “mye”, “også”, “mens”
    sentence ← Convert sentence to lowercase
    nynorskCount ← 0
    bokmaalCount ← 0
    for marker ∈ nynorskMarkers do
        if marker exists as whole word in sentence then
            nynorskCount ← nynorskCount + 1
        end if
    end for
    for marker ∈ bokmaalMarkers do
        if marker exists as whole word in sentence then
            bokmaalCount ← bokmaalCount + 1
        end if
    end for
    nynorskCount ← nynorskCount+ Count of words ending in ’a’
    bokmaalCount ← bokmaalCount+ Count of words ending in ’en’
    if nynorskCount > bokmaalCount then
        return “Nynorsk”
    else if bokmaalCount > nynorskCount then
        return “Bokmål”
    else if nynorskCount = bokmaalCount and nynorskCount > 0 then
        return “Mixed”
    else
        return “Unmarked / Neutral”
    end if
end procedure
Algorithm 2 Arabic Fusha / Dialect Classification Logic

procedure IsFUSHA(sentence)
    fushaMarkers ← List of Fusha (Modern Standard Arabic) markers
    dialectMarkers ← List of dialectal Arabic markers
    fushaScore ← 0
    dialectScore ← 0
    for each marker in fushaMarkers do
        if marker found in sentence then
            fushaScore ← fushaScore + 1
        end if
    end for
    for each marker in dialectMarkers do
        if marker found in sentence then
            dialectScore ← dialectScore + 1
        end if
    end for
    if fushaScore = 0 and dialectScore = 0 then
        return "unmarked"
    else if fushaScore > dialectScore then
        return "fusha"
    else if fushaScore < dialectScore then
        return "dialect"
    else
        return "mixed"
    end if
end procedure
J  Languages Inspected by Native Speaker Volunteers in this Study

We asked native speaker volunteers from our co-workers to review 100 randomly sampled sentences (text and audio) for coherence, audio-text alignment, dialect, topic domain, and language ID from each language subsets of the datasets. The language list is shown in 16.

<table>
  <tr>
    <th>Name</th>
    <th>MCV17 code</th>
    <th>FLEURS code</th>
    <th>Name</th>
    <th>MCV17 code</th>
    <th>FLEURS code</th>
  </tr>
  <tr>
    <td>Amharic</td>
    <td>am</td>
    <td>am_et</td>
    <td>Lingala</td>
    <td></td>
    <td>ln_cd</td>
  </tr>
  <tr>
    <td>Arabic (Egypt)</td>
    <td>ar</td>
    <td>ar_eg</td>
    <td>Mandarin Chinese (China)</td>
    <td>zh_cn</td>
    <td>cmn_hans_cn</td>
  </tr>
  <tr>
    <td>Basaa</td>
    <td>bas</td>
    <td></td>
    <td>Mandarin Chinese (Taiwan)</td>
    <td>zh_tw</td>
    <td></td>
  </tr>
  <tr>
    <td>Bashkir</td>
    <td>ba</td>
    <td></td>
    <td>Meadow Mari</td>
    <td>mhr</td>
    <td></td>
  </tr>
  <tr>
    <td>Cantonese</td>
    <td>yue</td>
    <td>yue_hk</td>
    <td>Moksha</td>
    <td>mdf</td>
    <td></td>
  </tr>
  <tr>
    <td>Cape Verde Creole</td>
    <td>kea_cv</td>
    <td></td>
    <td>Norwegian (Nynorsk / Bokmål)</td>
    <td>nn_no</td>
    <td>nb_no</td>
  </tr>
  <tr>
    <td>Catalan</td>
    <td>ca</td>
    <td>ca_es</td>
    <td>Oromo</td>
    <td></td>
    <td>om_et</td>
  </tr>
  <tr>
    <td>Chichewa</td>
    <td>ny_mw</td>
    <td></td>
    <td>Persian</td>
    <td>fa</td>
    <td>fa_ir</td>
  </tr>
  <tr>
    <td>Chinese (Hong Kong)</td>
    <td>zh_hk</td>
    <td></td>
    <td>Russian</td>
    <td>ru</td>
    <td>ru_ru</td>
  </tr>
  <tr>
    <td>Chuvash</td>
    <td>cv</td>
    <td></td>
    <td>Shona</td>
    <td></td>
    <td>sn_zw</td>
  </tr>
  <tr>
    <td>Danish, Denmark</td>
    <td>da</td>
    <td>da_dk</td>
    <td>Southern Min</td>
    <td>nan_tw</td>
    <td></td>
  </tr>
  <tr>
    <td>Dyula</td>
    <td>dyu</td>
    <td></td>
    <td>Spanish</td>
    <td>es</td>
    <td></td>
  </tr>
  <tr>
    <td>English (United States)</td>
    <td>en</td>
    <td>en_us</td>
    <td>Swahili</td>
    <td>sw</td>
    <td>sw_ke</td>
  </tr>
  <tr>
    <td>Fulah (Senegal)</td>
    <td>ff_sn</td>
    <td></td>
    <td>Twi</td>
    <td>tw</td>
    <td></td>
  </tr>
  <tr>
    <td>Ganda</td>
    <td>lg</td>
    <td>lg_ug</td>
    <td>Ukrainian</td>
    <td>uk</td>
    <td></td>
  </tr>
  <tr>
    <td>German</td>
    <td>de</td>
    <td></td>
    <td>Votic</td>
    <td>vot</td>
    <td></td>
  </tr>
  <tr>
    <td>Greek</td>
    <td>el</td>
    <td>el_gr</td>
    <td>Western Mari</td>
    <td>mrj</td>
    <td></td>
  </tr>
  <tr>
    <td>Hausa</td>
    <td>ha_ng</td>
    <td></td>
    <td>Wolof</td>
    <td></td>
    <td>wo_sn</td>
  </tr>
  <tr>
    <td>Igbo</td>
    <td>ig_ng</td>
    <td></td>
    <td>Yoruba</td>
    <td></td>
    <td>yo_ng</td>
  </tr>
  <tr>
    <td>Japanese</td>
    <td>ja</td>
    <td>ja_jp</td>
    <td>Zulu</td>
    <td>zu</td>
    <td>zu_za</td>
  </tr>
  <tr>
    <td>Korean</td>
    <td>ko</td>
    <td>ko_kr</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 16: Languages with native speaker volunteers available for qualitative inspections in this study.

K  Acknowledgments

The authors gratefully acknowledge the helpful comments from Daan van Esch, Sandy Ritchie, and the consults by Jordan Sihno Mbeleg, Fafi Getachew, Issagha Ba, Ali Konaté, Geraldine Nalubeg, Zacharie Liman-Tinguiri, Chike Ezeokoli, Chuck Onwuzuru, Hernani Delgado Chan, Anya Mhone, Senzeni Mpofu, Isaac Ackah, Isaac Attuah, Temi Adesina, Fatou Fall, Michael Asare, Temi Adesina, Marvin Ngobeni and Parisa Haghani.

Reviewer paper summaries: The submission provides a very detailed quality audit of three multilingual speech datasets to ascertain the quality and extent of the respective datasets. The audit shows various quality issues with the respective datasets, especially for lower-resourced languages.
Summary of strengths: The audit is a very thorough review of the data and highlights different micro and macro issues with the respective data sets such as short durations, low proportional speech, imbalanced domains, differing writing systems, ambiguous registers, and the fact that for many languages there are not enough different speakers.
The submission makes concrete proposals for improving the datasets themselves, or at least providing the necessary metadata for users to make informed decisions about the use of the datasets.
Summary of weaknesses: The submission is very thorough, and this reviewer could not find any real weaknesses that detracts from the quality of the work.
Comments, suggestions and typos: None
Overall assessment scores: 4.5
Metareview score: 4
Metareview: This study highlights significant quality issues in widely used speech corpora, addressing concerns such as inconsistent writing systems, ambiguous register distinctions, limited speaker representation, and high noise-to-speech ratios. Identifying these problems is crucial given the widespread reliance on these datasets. The study also offers language-specific examples and suggests potential solutions to mitigate these challenges. It is grounded in a solid methodology and sheds light on real-world obstacles. However, there are some limitations: although the author discusses the implications of these quality issues for automatic speech recognition (ASR), no ASR results are presented. Additionally, some concerns may not be universally relevant—for example, in certain cases, a high speech-to-noise ratio can actually enhance ASR performance, and speech concatenation techniques can help address extremely short utterances. Finally, actionable solutions are lacking in some instances, particularly for languages that lack strong institutional support.
Summary of reasons to publish: - Provides a thorough analysis of major speech datasets
- Highlights key quality issues, including those affecting low-resource languages
- Proposes several solutions to address the identified problems