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  "path": "/t/opensource-llms-for-audio-understanding-british-accents/175528#post_2",
  "publishedAt": "2026-04-25T07:16:29.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "ACL Anthology",
    "Edinburgh DataShare",
    "ISCA Archive",
    "arXiv",
    "Hugging Face"
  ],
  "textContent": "If you plan to use the dataset for multiple purposes—such as for ASR, TTS, or variations like multimodal LLMs—creating rich metadata (labels) for the dataset during its creation will give you more options later on regarding which model to train and for what purpose. (Rather than the dataset having a fixed structure, you select exactly which data to use and how to use it from within the dataset via settings or scripts immediately before fine-tuning; therefore, the more clues the dataset provides, the more versatile it becomes.)\n\nIt’s a trade-off with the effort required for labeling, though…\n\n* * *\n\n## Core recommendation\n\nYour dataset should be framed as more than an ASR dataset. The strongest contribution is:\n\n> **A British Accent Audio Understanding Benchmark for open ASR models, speech encoders, accent classifiers, and audio-LLMs.**\n\nThat framing lets you evaluate five things separately:\n\nCapability | Core question | Model types\n---|---|---\n**ASR** | What words were spoken? | Whisper, Qwen3-ASR, Parakeet, Canary, Voxtral transcription mode\n**Accent ID** | Which British accent group is this? | WavLM, XLS-R, ECAPA-TDNN, Whisper encoder + classifier\n**Audio-LLM understanding** | Can the model listen and answer structured questions about transcript, accent, pronunciation, and uncertainty? | Voxtral, Qwen2.5-Omni, Phi-4-multimodal, Kimi-Audio, Ultravox\n**Pronunciation probing** | Can the model hear British-specific phonetic features? | Audio-LLMs, speech encoders, probe classifiers\n**Fairness / robustness** | Which accent/gender groups are underserved? | Any ASR or audio model\n\nYour dataset is valuable because it combines **audio + transcript + accent + gender**. That combination supports transcription, classification, instruction-following audio tasks, and group-level fairness analysis in one benchmark.\n\nA closely related British Isles accent corpus contains **31+ hours** from **120 volunteers** self-identifying as speakers of **Southern England, Midlands, Northern England, Welsh, Scottish, and Irish** English varieties, showing that this scale is credible for British-accent speech research. (ACL Anthology)\n\n* * *\n\n# 1. Why this is timely\n\n## English ASR is not the same as British-accent ASR\n\nGeneral English ASR benchmarks often underrepresent accent diversity. EdAcc was created to better represent global English variation and contains almost **40 hours** of dyadic English conversations from speakers with diverse accents and linguistic backgrounds. (Edinburgh DataShare)\n\nSo your research question should not be:\n\n> Can the model transcribe English?\n\nIt should be:\n\n> Can open ASR and audio-LLM systems handle **British regional accents** fairly, robustly, and transparently?\n\n## Accent errors can be linguistically meaningful\n\nA Newcastle English ASR error-analysis paper links ASR errors to regional dialectal features, including phonological, lexical, and morphosyntactic variation. That supports going beyond WER into error categories such as vowel differences, dialect words, local pronouns, and place-name recognition. (ISCA Archive)\n\nUseful error categories for your work:\n\nError type | Example\n---|---\n**Phonological** | TRAP–BATH, rhoticity, glottal /t/\n**Lexical** | “nowt” → “not” or “nothing”\n**Morphosyntactic** | “yous” → “you”\n**Named entity** | local town/station name mistranscribed\n**Over-normalization** | dialect form converted to standard English\n**Hallucination** | extra phrase inserted after silence\n\n* * *\n\n# 2. Existing benchmarks to connect to\n\nThere are relevant open audio/speech benchmarks, but none fully covers **British regional accent understanding**. That gap is your opportunity.\n\nBenchmark / resource | What it gives you | How to use it\n---|---|---\n**AudioBench** | AudioLLM benchmark with **8 tasks** and **26 datasets** across speech, audio scenes, and paralinguistic/voice understanding. (arXiv) | Use as the main precedent for audio-LLM evaluation beyond ASR.\n**AIR-Bench** | Large audio-language model benchmark with **19 tasks / ~19k multiple-choice questions** plus **~2k open-ended QA** examples. (ACL Anthology) | Use for multiple-choice accent and pronunciation probes.\n**Dynamic-SUPERB Phase 2** | Instruction-based speech/audio benchmark expanded to **180 tasks**. (arXiv) | Use as a template for “listen and classify / transcribe / answer” tasks.\n**Open ASR Leaderboard** | Reproducible ASR benchmark comparing **60+ systems** across **11 datasets** , with WER and RTFx. (Hugging Face) | Use for ASR-style reporting: WER + speed + standardized normalization.\n**CommonAccent** | Accent-classification recipe using ECAPA-TDNN and Wav2Vec2/XLS-R on Common Voice. (arXiv) | Use for accent-ID baselines and classifier design.\n**ASR-FAIRBENCH** | Fairness-aware ASR benchmark combining WER with a fairness score. (arXiv) | Use for accent/gender fairness framing.\n**Vox-Profile** | Speaker/speech trait benchmark including accent, sex, age, voice quality, emotion, and speech flow. (arXiv) | Use as precedent for treating accent/gender as speech traits, with careful ethics.\n\n* * *\n\n# 3. Recommended benchmark tracks\n\n## Track 1 — ASR robustness\n\n**Question:** How well do open ASR models transcribe different British accents?\n\nEvaluate:\n\n\n    audio → transcript\n\n\nUse metrics beyond average WER:\n\nMetric | Why\n---|---\n**Overall WER / CER** | Basic transcription accuracy\n**WER by accent** | Core robustness measure\n**WER by gender** | Broad gender-related gap\n**WER by accent × gender** | Intersectional gap\n**Macro-accent WER** | Treats accents equally\n**Worst-accent WER** | Finds most underserved group\n**Accent WER gap** | Worst minus best\n**Substitution / deletion / insertion rates** | Separates wrong words, missed words, hallucinations\n**Dialect-word recall** | Tests words like “nowt,” “aye,” “wee,” etc.\n**Place-name recall** | Tests local named entities\n**RTFx / latency** | Practical deployability\n\nThis track answers:\n\n> Which open models transcribe British-accented speech best, and which accent groups remain hard?\n\n* * *\n\n## Track 2 — Accent identification\n\n**Question:** Can models identify British regional accent groups from raw audio?\n\nEvaluate:\n\n\n    audio → accent_label\n\n\nUse broad labels for the official benchmark:\n\n\n    Southern England\n    Midlands\n    Northern England\n    Welsh\n    Scottish\n    Irish\n    Northern Irish\n    uncertain / other\n\n\nKeep fine labels as metadata:\n\n\n    accent_label_broad = \"Northern England\"\n    accent_label_fine = \"Newcastle / Tyneside\"\n    accent_self_reported = \"Geordie\"\n\n\nRecommended baseline ladder:\n\nLevel | Model | Why\n---|---|---\n0 | Majority-class baseline | Sanity check\n1 | MFCC + logistic regression / SVM | Classical baseline\n2 | ECAPA-TDNN embeddings + classifier | Strong speaker/acoustic embedding baseline\n3 | WavLM embeddings + classifier | Strong speech representation baseline\n4 | XLS-R / Wav2Vec2 embeddings + classifier | Cross-lingual speech representation baseline\n5 | Whisper encoder embeddings + classifier | Tests whether ASR encoders preserve accent cues\n6 | Fine-tuned WavLM / XLS-R | Strong supervised classifier\n7 | Audio-LLM prompted classifier | Tests instruction-following without a classifier head\n\nCommonAccent provides a direct precedent for ECAPA-TDNN and Wav2Vec2/XLS-R accent-classification recipes. (arXiv)\n\nUse metrics:\n\n\n    accuracy\n    macro-F1\n    balanced accuracy\n    per-accent recall\n    top-2 accuracy\n    confusion matrix\n    confidence calibration\n\n\n**Important:** official results must use **speaker-disjoint splits**. Random clip splits can leak speaker identity and inflate accent-classification scores.\n\n* * *\n\n## Track 3 — Accent-aware ASR\n\n**Question:** Can an audio-LLM transcribe speech and identify the accent in one structured response?\n\nEvaluate:\n\n\n    audio + instruction → transcript + accent_label + confidence + evidence\n\n\nExample output:\n\n\n    {\n      \"transcript\": \"I went down to the station this morning.\",\n      \"accent_label\": \"Northern England\",\n      \"confidence\": 0.72,\n      \"evidence\": [\n        \"short BATH vowel\",\n        \"non-rhotic pronunciation\",\n        \"regional vowel quality\"\n      ]\n    }\n\n\nSuggested prompt:\n\n\n    Listen to the audio and return only valid JSON.\n\n    Use this schema:\n    {\n      \"transcript\": string,\n      \"accent_label\": one of [\n        \"Southern England\",\n        \"Midlands\",\n        \"Northern England\",\n        \"Welsh\",\n        \"Scottish\",\n        \"Irish\",\n        \"Northern Irish\",\n        \"uncertain\"\n      ],\n      \"confidence\": number from 0 to 1,\n      \"evidence\": list of at most 3 short strings\n    }\n\n    If the accent is unclear, use \"uncertain\".\n    Do not invent labels.\n\n\nMetrics:\n\nMetric | Why\n---|---\n**Transcript WER** | ASR quality\n**Accent accuracy** | Accent-label correctness\n**Joint score** | Transcript acceptable + accent correct\n**Valid JSON rate** | Instruction-following reliability\n**Allowed-label rate** | Whether the model obeys the label set\n**Confidence calibration** | Whether confidence matches correctness\n**Evidence quality** | Whether explanations are plausible\n**Uncertainty behavior** | Whether “uncertain” is used appropriately\n\nThis is probably the cleanest LLM-focused task in your project.\n\n* * *\n\n## Track 4 — Pronunciation probes\n\n**Question:** Can models hear British-English pronunciation features?\n\nASR alone cannot capture this. Two speakers may both say **“bath”** , but one may use a short TRAP vowel and another a long BATH/PALM vowel. The transcript is identical; the pronunciation is not.\n\nUseful probes:\n\nFeature | Example words | Task\n---|---|---\n**TRAP–BATH split** | bath, path, grass, dance | short TRAP vs long BATH/PALM\n**Rhoticity** | car, park, hard, farm | /r/ pronounced or not\n**Glottal /t/** | water, bottle, little | glottalized /t/ or not\n**STRUT vowel** | cup, luck, strut | northern/southern-style vowel quality\n**Dialect words** | nowt, owt, aye, wee | which word was spoken\n**Local pronouns** | yous, wor | which form was spoken\n**Place names** | local towns/stations | which place name was spoken\n\nExample multiple-choice tasks:\n\n\n    In the word \"bath\", does the speaker use:\n    A. short TRAP vowel\n    B. long BATH/PALM vowel\n    C. unclear\n\n\n\n    In the word \"car\", is the /r/ pronounced?\n    A. yes\n    B. no\n    C. unclear\n\n\n\n    Which word was spoken?\n    A. nothing\n    B. nowt\n    C. not\n    D. unclear\n\n\nMetrics:\n\n\n    overall probe accuracy\n    per-feature accuracy\n    per-accent accuracy\n    abstention rate\n    human agreement\n\n\nThis track is where your dataset becomes more than an ASR benchmark. It becomes a **British accent understanding** benchmark.\n\n* * *\n\n## Track 5 — Fairness and group robustness\n\n**Question:** Which accent/gender groups are underserved?\n\nUse gender mainly as evaluation metadata, not necessarily as a prediction target.\n\nRecommended metrics:\n\nMetric | Meaning\n---|---\n**Overall WER** | Average transcription quality\n**Macro-accent WER** | Treats accents equally\n**Worst-accent WER** | Most underserved accent\n**Best-accent WER** | Easiest accent\n**Accent WER gap** | Worst minus best\n**Accent WER ratio** | Worst divided by best\n**Gender WER gap** | Difference by gender\n**Accent × gender WER gap** | Intersectional difference\n**Worst-accent recall** | Accent classifier’s weakest group\n**Calibration by group** | Whether confidence is equally reliable\n\nThis matters because average WER can hide harm:\n\nModel | Overall WER | Worst-accent WER | Accent gap\n---|---|---|---\nModel A | 7.8 | 18.5 | 13.2\nModel B | 8.4 | 13.1 | 7.4\n\nModel A looks better on average; Model B may be more inclusive.\n\nASR-FAIRBENCH gives a useful precedent for combining accuracy and equity in ASR evaluation. (arXiv)\n\n* * *\n\n# 4. The key LLM experiment: audio-only vs transcript-only\n\nThis is the experiment I would emphasize most.\n\n## Core question\n\n> Do audio-LLMs actually hear accent cues, or do they infer accent from words in the transcript?\n\nRun four conditions:\n\nCondition | Input | What it tests\n---|---|---\n**Audio only** | audio | Can the model hear accent directly?\n**Gold transcript only** | human transcript | Can text alone reveal accent from dialect words?\n**ASR transcript only** | model transcript | How strong is a standard ASR → LLM pipeline?\n**Audio + transcript** | both | Does multimodal input help?\n\nWhy this matters:\n\nA text-only LLM may guess **Scottish** from words like:\n\n\n    wee\n    aye\n    ken\n\n\nor **Northern England** from:\n\n\n    nowt\n    owt\n    yous\n\n\nBut that does not prove it heard the accent. The audio-only condition tests whether the model uses acoustic information. This ablation would make your LLM contribution much stronger.\n\n* * *\n\n# 5. Model families worth testing\n\n## ASR models\n\nModel family | Why include\n---|---\n**Qwen3-ASR** | Current open ASR family; Qwen3-ASR supports language identification and ASR for **52 languages and dialects**. (Hugging Face)\n**Whisper / Whisper turbo** | Standard ASR baseline family.\n**Distil-Whisper** | Efficient English ASR baseline.\n**NVIDIA Parakeet / Canary** | Non-Whisper ASR baselines; useful for speed/accuracy diversity.\n**Voxtral transcription mode** | Audio-LLM with dedicated transcription mode and long-context audio support. (Hugging Face)\n\n## Audio-LLMs\n\nModel | Why include\n---|---\n**Voxtral Mini / Small** | Audio model with transcription, Q&A, summarization, and long-context audio support. (Hugging Face)\n**Qwen2.5-Omni** | General multimodal audio/video/text model.\n**Phi-4-multimodal-instruct** | Compact multimodal model with audio input.\n**Kimi-Audio-7B-Instruct** | Open audio foundation model for understanding, generation, and conversation.\n**Ultravox** | Direct speech-to-LLM style model.\n\n## Accent classifiers / encoders\n\nModel type | Why include\n---|---\n**MFCC + SVM/logistic regression** | Simple baseline\n**ECAPA-TDNN embeddings** | Strong speaker/acoustic embedding baseline\n**WavLM embeddings** | Strong speech representation baseline\n**XLS-R / Wav2Vec2 embeddings** | Cross-lingual speech representation baseline\n**Whisper encoder embeddings** | Tests whether ASR features contain accent information\n**Fine-tuned WavLM / XLS-R** | Likely strong supervised accent classifiers\n\nWavLM is a good classification base because its model card describes it as an English pretrained speech model intended for downstream tasks including speech recognition and audio classification. (Hugging Face)\n\n* * *\n\n# 6. Dataset design essentials\n\n## Use speaker-disjoint splits\n\nBad:\n\n\n    Speaker 001 clip 1 → train\n    Speaker 001 clip 2 → test\n\n\nGood:\n\n\n    Speaker 001 → train only\n    Speaker 024 → validation only\n    Speaker 047 → test only\n\n\nThis is non-negotiable for accent classification. Otherwise the model may learn speaker identity, microphone, room acoustics, or session artifacts.\n\n## Keep raw and normalized transcripts\n\nExample:\n\n\n    Raw:        \"Aye, I've got nowt to do with it.\"\n    Normalized: \"aye ive got nowt to do with it\"\n\n\nDo not normalize dialect words into standard English:\n\n\n    nowt → nothing\n    aye → yes\n    wee → small\n\n\nThat would erase the phenomenon you want to study.\n\n## Store broad + fine accent labels\n\n\n    accent_label_broad = \"Northern England\"\n    accent_label_fine = \"Newcastle / Tyneside\"\n    accent_self_reported = \"Geordie\"\n    accent_label_source = \"self-reported\"\n\n\n## Add prompt-level metadata\n\nField | Example\n---|---\n`prompt_id` | `bath_001`\n`target_words` | `bath,path,grass`\n`target_feature` | `TRAP_BATH`\n`contains_dialect_word` | `true`\n`contains_place_name` | `true`\n\n## Release in a no-script Hugging Face format\n\nUse **Parquet** or **AudioFolder** with metadata CSV/JSONL. Hugging Face’s `AudioFolder` is designed to load audio datasets with thousands of files without requiring custom code. Dataset cards are also recommended to document contents, creation process, use context, and potential biases. (Hugging Face)\n\nSuggested fields:\n\nField | Purpose\n---|---\n`audio_id` | Unique clip ID\n`speaker_id_hash` | Pseudonymous speaker ID\n`split` | Train/validation/test\n`audio` | Audio file or audio column\n`duration_sec` | Clip duration\n`transcript_raw` | Original transcript\n`transcript_normalized` | WER transcript\n`accent_label_broad` | Main benchmark label\n`accent_label_fine` | Optional detailed label\n`accent_self_reported` | Original self-description\n`accent_label_source` | Label provenance\n`gender_self_described` | Fairness slicing, if consented\n`target_feature` | Pronunciation feature\n`recording_condition` | Clean/noisy/phone/unknown\n`consent_scope` | Usage permission summary\n\n* * *\n\n# 7. Suggested result tables\n\n## ASR summary\n\nModel | Overall WER | Macro-accent WER | Worst-accent WER | Accent gap | RTFx\n---|---|---|---|---|---\nQwen3-ASR-1.7B |  |  |  |  |\nParakeet |  |  |  |  |\nCanary-Qwen |  |  |  |  |\nWhisper large-v3 |  |  |  |  |\nWhisper large-v3-turbo |  |  |  |  |\nDistil-Whisper |  |  |  |  |\nVoxtral Mini |  |  |  |  |\n\n## Accent classification\n\nModel | Accuracy | Macro-F1 | Balanced accuracy | Worst-accent recall | Top-2 accuracy\n---|---|---|---|---|---\nMajority baseline |  |  |  |  |\nMFCC + SVM |  |  |  |  |\nECAPA + classifier |  |  |  |  |\nWavLM + classifier |  |  |  |  |\nXLS-R + classifier |  |  |  |  |\nWhisper encoder + classifier |  |  |  |  |\nAudio-LLM prompt |  |  |  |  |\n\n## Audio-LLM ablation\n\nModel | Audio only | Gold transcript only | ASR transcript only | Audio + transcript\n---|---|---|---|---\nVoxtral Mini |  |  |  |\nQwen2.5-Omni |  |  |  |\nPhi-4-multimodal |  |  |  |\nKimi-Audio |  |  |  |\n\nThis last table is the most important one for your LLM-focused contribution.\n\n* * *\n\n# 8. Contribution statements you can use\n\n## Dataset contribution\n\n> We release a speaker-disjoint British regional accent speech dataset with audio, transcripts, gender metadata, accent labels, raw transcripts, normalized transcripts, and benchmark splits.\n\n## Benchmark contribution\n\n> We introduce a benchmark for British accent audio understanding, covering ASR robustness, accent identification, accent-aware ASR, pronunciation probes, and accent × gender fairness.\n\n## Audio-LLM contribution\n\n> We test whether audio-LLMs identify accents from acoustic cues or infer them from transcript/dialect words by comparing audio-only, gold-transcript-only, ASR-transcript-only, and audio+transcript settings.\n\n## Fairness contribution\n\n> We show that average WER is insufficient for British-accent evaluation and report macro-accent WER, worst-accent WER, accent gap, and accent × gender gaps.\n\n## Linguistic contribution\n\n> We connect model errors to British English pronunciation and dialect features, including TRAP–BATH variation, rhoticity, glottal /t/, dialect words, local pronouns, and place names.\n\n* * *\n\n# 9. Good title options\n\nTitle | Emphasis\n---|---\n**British Accent Audio Understanding Benchmark** | General benchmark framing\n**Benchmarking Open ASR Models and Audio-LLMs on British Regional Accents** | Model evaluation\n**British Regional Accent Robustness for ASR and Audio-Language Models** | Robustness/fairness\n**Accent-Aware Speech Recognition and Audio Understanding for British English** | ASR + audio-LLM\n**Do Audio-LLMs Hear British Accents?** | LLM-focused\n**Beyond WER: Evaluating British Accent Understanding in Open Speech Models** | Pronunciation/fairness\n\n* * *\n\n# Final compact recommendation\n\nThe strongest version of your project is:\n\n> **A speaker-disjoint British Accent Audio Understanding Benchmark with ASR, accent ID, accent-aware ASR, pronunciation probes, audio-only vs transcript-only LLM ablations, and accent × gender fairness metrics.**\n\nThis is stronger than a simple ASR dataset because it asks:\n\n  1. Can the model transcribe?\n  2. Can the model detect accent?\n  3. Can the model identify pronunciation features?\n  4. Can the model reason over audio with structured prompts?\n  5. Can the model perform fairly across accent and gender groups?\n  6. Can audio-LLMs really use audio, or are they relying on transcript clues?\n\n\n\n**Short summary**\n\n  * Do ASR, but do not stop at ASR.\n  * Use audio classifiers for accent ID.\n  * Use audio-LLMs for structured accent-aware tasks.\n  * Add pronunciation probes to make the dataset linguistically meaningful.\n  * Use speaker-disjoint splits.\n  * Report per-accent, per-gender, and accent × gender metrics.\n  * Keep raw + normalized transcripts.\n  * Release in Parquet or AudioFolder with a strong dataset card.\n  * Delay full audio-LLM fine-tuning until zero-shot results show a clear failure mode.\n\n",
  "title": "Opensource LLMs for Audio Understanding - British Accents"
}