---
title: 'EdAcc: Edinburgh Accents Corpus'
url: https://www.emergentmind.com/topics/edacc-dataset
type: topic
---

# EdAcc: Edinburgh Accents Corpus

The Edinburgh International Accents of English Corpus (EdAcc) is a spontaneous, dyadic conversational speech dataset designed to address critical gaps in English speech corpora for automatic speech recognition (ASR), speaker anonymization, and accent-robust language technology development. Distinguished by its sociolinguistic diversity, conversational speech style, and meticulous speaker metadata, EdAcc is structured to minimize lexical content-based speaker leakage—a pronounced defect in benchmark corpora such as LibriSpeech—while supplying a robust resource for the analysis and evaluation of ASR and speaker-privacy algorithms across a globally representative spectrum of English varieties [2601.13107][2303.18110].

## 1. Origin and Goals

EdAcc was created to remedy limitations in prevalent datasets for ASR and speaker-anonymization evaluation—chiefly, the overrepresentation of US/UK native-read speech and the risk of content-induced speaker disclosure. In LibriSpeech, for example, speakers read unique books, making vocabulary distributions speaker-specific and enabling content-driven identity leakage even through “perfect” acoustic anonymization systems. EdAcc’s primary objectives are:
- To provide spontaneous, dyadic, conversational English speech as opposed to read speech.
- To encompass a broad array of English accents and first-language (L1) backgrounds, with representation from L1 and L2 speakers varying in age, ethnicity, and region.
- To mitigate vocabulary-based speaker identification and encourage evaluation of anonymization and ASR systems where acoustic and paralinguistic cues, rather than lexical content, drive model performance [2601.13107].

## 2. Dataset Composition and Speaker Diversity

EdAcc comprises nearly 40 hours of wide-band English conversational audio, drawn from 61 Zoom video calls (each 20–60 minutes) between friend/acquaintance pairs. No speaker appears in both the official development (31 conversations, ∼20 h) and test (30 conversations, ∼19 h) sets, resulting in approximately 122 unique participants [2303.18110]. The released subsets for privacy experiments total about 22 hours (dev ∼11 h, test ∼11 h).

Speaker and linguistic diversity is central to the corpus:
- Over 40 reported English accents (L1 varieties), with at least 51 distinct L1 backgrounds among L2 speakers.
- Accented Englishes include, for example, American, Irish, Jamaican, Nigerian, and Spanish-accented English.
- Socio-demographic and biographical metadata per speaker: age bracket, gender, ethnicity, first/second languages, age/year of English acquisition (for L2), countries lived in, and self-ascribed accent.

Dyadic sessions elicit spontaneous discourse using open prompts, yielding natural prosody and interactional structure. Each participant also records the canonical “Stella” passage, facilitating accent-controlled phonetic analyses [2303.18110].

## 3. Data Structure, Annotation, and Access

Dataset releases comprise:
- Mono-channel, wide-band WAV audio (16 kHz), packaged by conversation ID.
- Orthographic, human-verified transcripts aligned at the utterance/turn level, annotated for disfluencies, laughter, and background sounds.
- Speaker metadata CSV files and data statements.
- Standard Kaldi-style directory structure (wav.scp, text, utt2spk, spk2utt) to maximize compatibility with ASR pipelines.

Annotation protocol includes manual segmentation, professional transcription with privacy compliance (source deletions within 10 days), and post-processing for lexical normalization and alignment with ASR lexicons [2303.18110]. All data are distributed under the Creative Commons Attribution-ShareAlike (CC-BY-SA) license and are downloadable from the project website (https://groups.inf.ed.ac.uk/edacc/).

## 4. Speaker Anonymization and Content Leakage Analysis

A primary application for EdAcc is the privacy evaluation of speaker-anonymization systems. The evaluation protocol follows VoicePrivacy 2024 using the SpAnE framework, ECAPA-TDNN speaker recognition (SpeechBrain), and the Equal Error Rate (EER) metric:
- Enrollment: 20 anonymized utterances/speaker → extract mean embedding.
- Trial: 20 anonymized utterances/speaker → embeddings compared to enrollment via cosine similarity.
- EER: rate at which False Accept = False Reject; 0% = full identification, 50% = random guessing (maximum privacy).

A “perfect” anonymizer is implemented as a speech-to-text-to-speech (STT-TTS) pipeline (Whisper-small → FastPitch+HiFi-GAN), eliminating all speaker-specific acoustic information so that only content remains as an identifier. Additionally, "phone-only" and "phones+durations" feature attacks isolate vocabulary- and temporal-pattern-based leakage:

$$
R_{i,t} = d_t \cdot \delta_{i, \phi(p_t)}
$$

where $R_{i,t}$ is the input matrix (vocabulary index $i$, frame $t$), $d_t$ is phone $p_t$ duration, $\phi(p_t)$ maps phone to $i$, and $\delta$ is the Kronecker delta [2601.13107].

## 5. Comparative Privacy Benchmarking: EdAcc vs. LibriSpeech

EdAcc is contrasted with LibriSpeech in terms of vulnerability to content-driven speaker recognition attacks. Key results:

| Features                  | LibriSpeech Original | LibriSpeech STT-TTS | EdAcc Original | EdAcc STT-TTS |
|---------------------------|---------------------|---------------------|----------------|---------------|
| Mel-spectrogram           | 0.4 %               | 34.8 %              | 6.5 %          | 45.9 %        |
| Phones + durations        | 23.7 %              | 34.5 %              | 39.0 %         | 45.0 %        |
| Phones only               | 30.4 %              | 32.3 %              | 42.1 %         | 48.5 %        |

On original speech with full acoustic features, LibriSpeech speakers are identified almost perfectly (0.4 % EER). Even after STT-TTS anonymization, content (vocabulary, phone durations) yields 34–35 % EER, indicating considerable content-based leakage. In contrast, EdAcc’s STT-TTS anonymized data pushes EER values to ≈45–48 %, approaching the 50 % “ideal” for perfect anonymization, indicating minimal content-driven identification [2601.13107].

Population-segment privacy analysis reveals that intra- and inter-accent EERs in EdAcc remain high: for example, American and Jamaican speakers yield intra- and inter-EERs ≈47–48 % and ≈42 % respectively, indicating substantial privacy within and across accent groups.

## 6. Robustness, Fairness, and ASR Benchmarking

EdAcc poses pronounced challenges to current ASR models. Baseline results include:

| Model                   | EdAcc dev | EdAcc test | Libri-test-clean | Libri-test-other |
|-------------------------|-----------|------------|------------------|------------------|
| Wav2vec2.0 (fine-tuned) | 33.4 %    | 36.1 %     | 2.9 %            | 5.6 %            |
| Commercial engine       | 17.9 %    | 18.7 %     | 3.8 %            | 7.4 %            |
| Whisper (large)         | 16.4 %    | 19.7 %     | 2.7 %            | 5.6 %            |

Whisper (large) achieves ∼19.7 % WER on EdAcc test—contrasting with its 2.7 % WER on US English read speech (LibriSpeech)—a ∼17 point gap. Performance degradation is especially severe for Indian, Jamaican, and Nigerian English speakers (e.g., WER > 21–22 % for these groups) [2303.18110]. This evidences the corpus’s utility for accent-fairness research and model adaptation.

## 7. Use Cases, Impact, and Future Directions

EdAcc facilitates diverse research avenues:
- Speaker-anonymization system evaluation insensitive to content leakage, directing attackers toward acoustic rather than lexical cues.
- Benchmarking, adaptation, and debiasing of ASR and TTS systems for inclusive performance across L1/L2 and minoritized accents.
- Analyses of accent classification, sociophonetic variation, and population-segment privacy (via intra-/inter-EER).
- Development and audit of voice conversion/cross-accent transfer models with spontaneous speech [2601.13107].

Potential extensions include expanding corpus hours and L2 speaker backgrounds, introducing more realistic/noisy recording settings, and releasing detailed phonetic alignments.

EdAcc is referenced as:
Sanabria, R. et al., “The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR,” ICASSP 2023 [2303.18110], with corpus access via https://groups.inf.ed.ac.uk/edacc/.

## References

- [2601.13107] Content Leakage in LibriSpeech and Its Impact on the Privacy Evaluation of Speaker Anonymization
- [2303.18110] The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR

Source: https://www.emergentmind.com/topics/edacc-dataset