BanglaTurn: A Benchmark and Whisper-Based Model for End-of-Turn Detection in Bangla Speech
cs.CL
Quick verdict
首个公开的孟加拉语话轮结束（turn-end）语料库；Whisper 模型在 319 个剪辑的测试集上比 Smart-Turn v3 高出 15 个百分点；研究坦诚，但依赖单一标注员，限制了结论的确定性。
Summary
本文介绍了 BanglaTurn，据称是首个用于孟加拉语话轮结束（end-of-turn）检测的公开语料库，并配套了一个任务模型。这 35,374 个剪辑（3-15 秒）来自公开的孟加拉语 YouTube 播客，经归一化为 16 kHz 单声道，通过 PyAnnote 说话人日志（diarization）和 Silero VAD 进行分割，并结合说话人日志规则与 Gemini 2.0 Flash Lite 生成标签；作者本人（母语者）随后对每个标签进行了核对。模型在 Common Voice Bangla 上微调了 Whisper Tiny 编码器，连接了 max、mean、attention 和 last-frame 池化，并添加了一个小分类头。在与训练完全隔离的播客中提取的 319 个类别平衡剪辑的测试集上，该模型达到 84.33% 的准确率（95% CI 80.3-88.1），而 Smart-Turn v3 为 69.28%（z = 4.50, p < 10⁻⁵），将漏失率（false negatives）从 51.57% 降至 7.55%，误报率（false positives）从 10.00% 升至 23.75%。INT8 量化将模型缩减了约 73%（至 39-40 MB），并将 CPU 延迟降至 165-170 ms，准确率损失为 1.26 个百分点。在测试播客上进行交叉验证的经典基线模型得分等于或低于 63.32%。作者坦诚指出了核心局限性：标签依赖于没有标注员间一致性（inter-annotator agreement）的单个标注员，测试集规模小且由同一人挑选，仅覆盖播客，且语料库构建中的伪影（artifact）导致尾部静音成为一个具有误导性的捷径线索。
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
4 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- BanglaTurn is the first public corpus and benchmark for end-of-turn detection in Bangla, with 35,374 human-verified clips and a pipeline transferable to other low-resource languages.
- A Whisper-tiny-encoder classifier with Bangla fine-tuning, multi-scale pooling and 2 unfrozen layers reaches 84.33% accuracy on a balanced held-out-podcast test set, 15.05 points above Smart-Turn v3 (z = 4.50, p < 10⁻⁵).
- Relative to Smart-Turn v3, the model cuts the false negative rate from 51.57% to 7.55% at the cost of the false positive rate rising from 10.00% to 23.75%.
- INT8 quantization compresses the final model by ~73% and cuts CPU latency to 165–170 ms with only a 1.26-point accuracy loss, though quantization sensitivity varies substantially by configuration.
Strengths
First-of-kind resource with transferable pipeline
BanglaTurn (§3) fills a documented gap: no prior public Bangla end-of-turn corpus; 35,374 samples built with a pipeline (PyAnnote 3.1 + Silero VAD + Gemini 2.0 Flash Lite pass + human check) that §8 explicitly positions for reuse on other low-resource languages.
Unusually candid limitation handling
§7.2 self-reports the trailing-silence artifact (median 0.001 s vs 0.051 s), §7.3 concedes unmeasurable label error and annotator-selection effects, §8.2 lists domain and single-threshold limits — transparency that lets readers properly discount the 84.33% headline.
Statistical care within constraints
Deployment-relevant quantization analysis
Weaknesses
Test set small and subjectively curated
Label quality unbounded
Construction artifact enables shortcut learning
Single operating point and narrow domain
Reproducibility gaps
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Scope & Claims
The paper contributes (1) BanglaTurn, claimed as the first public corpus for end-of-turn detection in Bangla — 35,374 clips of 3–15 s from public Bangla YouTube podcasts, with binary turn-state labels produced by diarization plus a Gemini 2.0 Flash Lite pass and then checked by a single human (the author) — and (2) a Whisper-tiny-encoder classifier reported at 84.33% accuracy, 15.05 points above Smart-Turn v3, at CPU-usable latency. The claims are scoped honestly: §7.4 and §8.2 explicitly disclaim transfer to telephone or assistant audio, and §7.3 concedes the accuracy figure is partly agreement with one person's judgement.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 4 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is BanglaTurn and what is it for?
BanglaTurn is a dataset of 35,374 short (3–15 s) clips of Bangla conversational podcast speech, each labelled with whether the speaker finished their turn (endpoint) or continued speaking (non-endpoint). It supports building voice assistants and spoken dialogue systems that must decide when a Bangla-speaking user has finished talking — a capability the paper says no prior public Bangla dataset covered. The corpus is released through gated access on request, for research use.
How accurate is the BanglaTurn model compared to existing alternatives?
On a class-balanced 319-clip test set from a held-out podcast, the Whisper-encoder model reaches 84.33% accuracy (95% CI 80.3–88.1) versus 69.28% for the multilingual Smart-Turn v3 baseline. It misses far fewer genuine turn ends (7.55% vs 51.57% false negative rate) but interrupts more often (23.75% vs 10.00% false positive rate). After INT8 quantization it runs in 165–170 ms on CPU at 39–40 MB with roughly a 1.3-point accuracy cost.
Is the BanglaTurn dataset publicly available?
It is available through gated access granted on request, for research use only. The audio comes from publicly viewable YouTube podcasts; the authors release 3–15 s clips without speaker names or links back to source episodes, and speakers or rights holders can request removal. No code or trained model release is described in the paper.
What are the main limitations of this paper?
All labels were checked by a single annotator (the author), so there is no inter-annotator agreement figure and the label error rate is unknown. The test set is small (319 clips), handpicked as difficult by the same person, and drawn from one podcast. The corpus covers only wideband podcast speech, so accuracy on phone calls or voice-assistant audio is untested, and the way clips were cut makes trailing silence a misleading shortcut cue that could inflate results.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1–2 are present only as captions in the extracted text; the architecture and pipeline diagrams could not be inspected directly.
- The extracted text appears complete (all sections, tables and references present), but page-level formatting and any supplementary material could not be verified.
- No released code, weights or dataset snapshot could be inspected; availability and gating claims rest on the paper's text alone.
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