PashtoTTS-Bench: automated screening for low-resource non-Latin-script text-to-speech

• URL: http://arxiv.org/abs/2605.26978v1
• Date: Tue, 26 May 2026 13:03:20 GMT
• Title: PashtoTTS-Bench: automated screening for low-resource non-Latin-script text-to-speech
• Abstract summary: Text-to-speech evaluation for low-resource non-Latin-script languages can fail when it relies on a single ASR round-trip word error rate (WER)<n>We introduce INSV, a reporting framework that separates these cases.<n>We instantiate INSV-A as PashtoTTS-Bench, a dated benchmark for Pashto TTS.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-speech (TTS) evaluation for low-resource non-Latin-script languages can fail when it relies on a single ASR round-trip word error rate (WER). A system may produce no audio, speak a neighbouring language, preserve target script text only in an ASR transcript, or sound unnatural to native listeners. We introduce INSV (Intelligibility, Naturalness, Script fidelity, and Verification), a reporting framework that separates these cases. This paper reports INSV-A, the automated screening subset: synthesis completion, ASR WER/CER, transcript Script Fidelity Rate, and audio language identification. Native MOS and phonetic annotation are specified but not claimed in this release. We instantiate INSV-A as PashtoTTS-Bench, a dated benchmark for Pashto TTS. The April-May 2026 run evaluates Edge GulNawaz, Edge Latifa, OmniVoice clone, OmniVoice auto, and an Urdu negative control on 200 FLEURS and 200 filtered Common Voice 24 prompts. Under the independent omniASR_CTC_300M_v2, OmniVoice auto has the lowest WER (24.1% FLEURS, 27.4% CV24), followed by Edge GulNawaz (32.8%, 39.5%), Edge Latifa (35.6%, 47.7%), and OmniVoice clone (45.4%, 34.8%). WER below the natural-speech baseline reflects clean synthetic audio and should not be read as better than native speech. Whisper Large V3 returns 0.0% Pashto labels on checked Pashto TTS audio, while MMS-LID-4017 and SpeechBrain VoxLingua107 separate Pashto outputs from the Urdu control. The release provides provider metadata, per-sentence scores, LID audits, failure logs, and scripts for adding systems.

Related papers

• RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems [5.434878413067881]
  We introduce the Real World Voice EQ Bench, a multidimensional benchmark for evaluating voice AI across text-to-speech (TTS), speech-to-speech (STS), speech understanding (SU), and automatic speech recognition (ASR)<n>Our evaluations indicate that performance is highly dimension-specific.
  arXiv  Detail & Related papers  (2026-07-16T11:15:16Z)
• Unified Audio Intelligence Without Regressing on Text Intelligence [86.55418188552768]
  We introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM.<n>Audex adopts a simple unified design with a single Transformer decoder.<n>Audex delivers state-of-the-art audio understanding, speech recognition and translation, text-to-speech, audio generation, and speech-to-speech generation.
  arXiv  Detail & Related papers  (2026-07-06T15:11:57Z)
• BaltiVoice: A Speech Corpus and Fine-tuned Whisper ASR System for the Balti Language [0.20771014670599888]
  We present BaltiVoice, a 16.8-hour read-speech corpus for Balti (ISO 639-3: bft), a Tibetic language spoken in Gilgit-Baltistan, Pakistan.<n>The corpus contains 10,060 validated utterances in native Nastaliq script, derived from Mozilla Common Voice recordings.<n>Fine-tuning OpenAI Whisper-small yields a Word Error Rate (WER) of 26.74% and a Character Error Rate (CER) of 8.67% on a 538-utterance speaker-disjoint validation set.
  arXiv  Detail & Related papers  (2026-06-02T11:23:49Z)
• X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning [62.740127542449166]
  X-Voice is a multilingual zero-shot voice cloning model that clones arbitrary voices and enables everyone to speak 30 languages.<n>X-Voice is trained on a 420K-hour multilingual corpus using the International Phonetic Alphabet (IPA) as a unified representation.
  arXiv  Detail & Related papers  (2026-05-07T02:57:53Z)
• BlasBench: An Open Benchmark for Irish Speech Recognition [0.0]
  Existing benchmarks include Irish among dozens of languages but apply no Irish-aware text normalisation.<n>We introduce BlasBench, an open evaluation harness that provides a standalone Irish-aware normaliser preserving fadas, lenition, and eclipsis.<n>We pilot this by benchmarking 12 systems across four architecture families on Common Voice ga-IE and FLEURS ga-IE.
  arXiv  Detail & Related papers  (2026-04-12T17:17:54Z)
• Benchmarking Multilingual Speech Models on Pashto: Zero-Shot ASR, Script Failure, and Cross-Domain Evaluation [0.0]
  Pashto is spoken by approximately 60--80 million people but has no published benchmarks for multilingual automatic speech recognition (ASR) on any shared public test set.<n>This paper reports the first reproducible multi-model evaluation on public Pashto data, covering zero-shot ASR, script-level failure, and cross-domain evaluation of fine-tuned models.
  arXiv  Detail & Related papers  (2026-04-06T11:23:42Z)
• Distilling an End-to-End Voice Assistant Without Instruction Training Data [53.524071162124464]
  Distilled Voice Assistant (DiVA) generalizes to Question Answering, Classification, and Translation. We show that DiVA better meets user preferences, achieving a 72% win rate compared with state-of-the-art models like Qwen 2 Audio.
  arXiv  Detail & Related papers  (2024-10-03T17:04:48Z)
• Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024 [61.189875635090225]
  Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST)
  arXiv  Detail & Related papers  (2024-06-24T16:38:17Z)
• XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception [62.660135152900615]
  Speech recognition and translation systems perform poorly on noisy inputs. XLAVS-R is a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation.
  arXiv  Detail & Related papers  (2024-03-21T13:52:17Z)
• A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition [0.0]
  Silent Speech Interfaces (SSIs) offer a noninvasive alternative to brain-computer interfaces for soundless verbal communication. We introduce Multimodal Orofacial Neural Audio (MONA), a system that leverages cross-modal alignment to train a multimodal model with a shared latent representation. To the best of our knowledge, this work represents the first instance where noninvasive silent speech recognition on an open vocabulary has cleared the threshold of 15% WER.
  arXiv  Detail & Related papers  (2024-03-02T21:15:24Z)
• Code-Mixed Text to Speech Synthesis under Low-Resource Constraints [6.544954579068865]
  We describe our approaches for production quality code-mixed Hindi-English TTS systems built for e-commerce applications. We propose a data-oriented approach by utilizing monolingual data sets in individual languages. We show that such single script bi-lingual training without any code-mixing works well for pure code-mixed test sets.
  arXiv  Detail & Related papers  (2023-12-02T10:40:38Z)
• Visual Speech Recognition for Languages with Limited Labeled Data using Automatic Labels from Whisper [96.43501666278316]
  This paper proposes a powerful Visual Speech Recognition (VSR) method for multiple languages. We employ a Whisper model which can conduct both language identification and audio-based speech recognition. By comparing the performances of VSR models trained on automatic labels and the human-annotated labels, we show that we can achieve similar VSR performance to that of human-annotated labels.
  arXiv  Detail & Related papers  (2023-09-15T16:53:01Z)
• BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric [66.73705349465207]
  End-to-end speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. We propose a text-free evaluation metric for end-to-end S2ST, named BLASER, to avoid the dependency on ASR systems.
  arXiv  Detail & Related papers  (2022-12-16T14:00:26Z)
• When Is TTS Augmentation Through a Pivot Language Useful? [26.084140117526488]
  We propose to produce synthetic audio by running text from the target language through a trained TTS system for a higher-resource pivot language. Using several thousand synthetic TTS text-speech pairs and duplicating authentic data to balance yields optimal results. Application of these findings improves ASR by 64.5% and 45.0% character error reduction rate (CERR) respectively for two low-resource languages.
  arXiv  Detail & Related papers  (2022-07-20T13:33:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.