Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.03718

NepTTS-Bench: Native-Speaker Benchmarking and NepaliMOS for Nepali Text-to-Speech

Ashish Thapa·Rupak Ghimire·Lekhnath Sharma Pathak
View original source ↗Read source document ↗Export metadata ↓

Abstract

Nepali TTS has reached a point where multiple systems are available, but there is no shared evaluation to compare them. NepTTS-Bench was created to address what existing automated and manual evaluation systems lack for Nepali TTS. A controlled benchmark with phonological grounding which contains 205 sentences (77 minimal pairs targeting aspiration, retroflex/dental contrasts, nasalization, gemination, and schwa deletion) which is rated by 180 speakers. There are 6,962 ratings for 10 TTS configurations and two human references. Along with this 7 ASR systems were evaluated for round-trip CER. SCOREQ was also run as an automated baseline. The key finding is that automated evaluation like SCOREQ prefers proprietary systems like ElevenLabs but native listeners prefer Piper. This is contributed by the fact that most of the systems not properly tuned for Nepali tend to show Hindi phonological behaviour and our minimal pairs are designed to surface such contrasts. We also present NepaliMOS predictor which is fine tuned on collected ratings. NepaliMOS achieves system-level rho = 0.90 on the dense panel but drops to utterance-level rho = 0.43 under leave-one-system-out evaluation. This is statistically significant and confirms that NepaliMOS captures genuine signal but cross-provider transfer is limited. A human calibration is recommended for new systems for which we release a Python evaluation package for practitioners to run the benchmark against new systems.

Explore related subjects

eess.AScs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ashish Thapa, Rupak Ghimire, Lekhnath Sharma Pathak. 2026-10-31. NepTTS-Bench: Native-Speaker Benchmarking and NepaliMOS for Nepali Text-to-Speech. https://arxiv.org/abs/2611.03718

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

NVV-Locator: From Transcript Tags to Acoustic Boundaries for Fine-Grained Nonverbal Vocalization Grounding

Human speech includes nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, which convey affective and interactional information. Existing approaches typically represent NVVs as transcript-level tags, providing limited supervision for their waveform-time boundaries. We present NVV-Locator for fine-grained NVV temporal grounding. We first unify 26 NVV categories across public resources and construct large-scale timestamp-supervised training data through dual-LLM verification, transcript-guided forced alignment, and energy-based boundary refinement. We further introduce NVV-TimeBench, an expert-refined benchmark with 667 utterances and 1,094 events. NVV-Locator uses a non-autoregressive slot-filling architecture to jointly predict lexical timestamps, NVV categories, and event boundaries. On NVV-TimeBench, it achieves 71.0% Micro F1, 70.2% Macro F1, 80.4% Macro mIoU, and 59.6 ms Macro mMAE, outperforming the evaluated large audio model counterparts. Evaluation on an external corpus further demonstrates the cross-corpus generalization of NVV-Locator.

eess.AS↗
arXiv2026

Attendance-guided speaker enrollment across meetings

Speaker-aware applications rely on enrolled voice profiles, yet collecting enrollment speech from every participant is impractical. Meeting archives provide an alternative source of enrollment speech, comprising recordings of recurring participants paired with expected attendance lists. We investigate whether expected attendance lists can be cross-referenced with diarized anonymous speaker clusters to identify reliable enrollment speech for recurring participants. The resulting task, multi-participant automatic enrollment, must accommodate two types of attendance noise: a listed participant may not speak, and a speaker may be unlisted. To this end, we introduce a benchmark for the task: a synthetic corpus calibrated to an in-house meeting corpus, paired with a contamination-sensitive evaluation protocol. The reference method we introduce scores cross-session cluster chains against expected-attendance patterns and abstains when the evidence is insufficient. We quantify the reference method's benchmark performance as a trade-off between enrollment success and contamination. Applying synthetic-development thresholds unchanged to real meetings exposes a synthetic-to-real gap that remains an open challenge. We will release the benchmark generator and evaluation code.

eess.AS↗
arXiv2026

VeriSpoof: Beyond Real or Fake with One Audio LLM

A speech deepfake detector estimates whether a recording is synthetic, but a forensic analyst also asks which system produced it, whether recordings share a source, and what evidence supports each conclusion. We present VeriSpoof, an audio language model that unifies five tasks with shared weights: deepfake detection, generator attribution (generator, acoustic model and vocoder), recording-condition analysis, synthesized-span localisation, and source verification. Each decision is supported by grounded rationales whose statements can be independently verified. Answers are scored by sentence likelihood, allowing detection and source verification to be thresholded without separate heads. Rationale statements can be checked against waveform measurements, span labels, or generator metadata. Counterfactual examples train decisions to follow the stated evidence. Across the 14 Speech-DF-Arena test sets, VeriSpoof-Plus achieves 2.14% pooled EER, ranking third of 28 systems and first among open-weight systems. VeriSpoof-Base, trained only on official training splits of eight corpora, achieves 4.43% EER, less than half that of the next open-weight detector. With the same weights, VeriSpoof-Plus verifies whether recordings from generators unseen in training share a source at 6.2% EER. Controlled studies show that the audio representation governs detection, the LLM governs recording comparison, and source supervision is necessary for neural-codec fakes. Statement-checkable rationales catch partial fakes missed by one-step answers (99.6% vs. 78.4% on PartialEdit) and determine decisions under edits unseen in training (99.5%). Training updates only 29.7M of 2.85B parameters, fitting a single 24 GB GPU with gradient checkpointing. We release both models, inference code, a demo, and VeriSpoof-Bench, the evaluation suite covering these questions.

eess.AS↗