arXiv Computation and Language
Quality-Aware Self-Correcting Speech Translation on an Edge Device
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv Machine Learning
Aug 5
M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
arXiv Computation and Language
Sep 23
Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation
arXiv:2609.26536v1 Announce Type: new Abstract: In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fi...
arXiv AI
2d ago
Refinement Buys Intelligibility, Search Buys Identity: What Test-Time Compute Buys in Masked-Diffusion TTS
The study investigates how two computational dimensions—model depth and refinement steps—affect intelligibility and speaker identity in masked-diffusion text‑to‑speech systems. Experiments with 15 models (19–133 M parameters) and up to 16 refinement steps show that refinement improves intelligibility more than identity, with a 1.86× asymmetry that persists even after retraining. Best‑of‑K search can recover identity when refinement fails, and analysis indicates that depth and steps target distinct bottlenecks, requiring separate optimization.
arXiv Computation and Language
2d ago
Learning When to Commit from Partial Speech for End-to-End Simultaneous Speech Translation
The paper presents a method for improving simultaneous speech translation by adapting a full‑utterance speech language model with prefix supervision derived from its own complete and partial waveform translations, eliminating the need for transcripts or human translations. Experiments on FLEURS and CoVoST2 across three language directions show that prefix training enhances quality–latency trade‑offs, especially when combined with multi‑turn append‑only decoding, and that a confidence threshold effectively controls the inference‑time quality–latency balance. The study also explores the impact of synthesis margin on translation quality and calibration, finding a non‑monotonic relationship with latency.
arXiv Computation and Language
Sep 25
Confident but Wrong: A Constrained Decoding Diagnostic for Low-Resource Automatic Post-Editing
The paper introduces a black‑box, inference‑time diagnostic for low‑resource Automatic Post‑Editing (APE) that distinguishes whether poor performance is due to insufficient training data or inconsistent training signals. By varying an edit‑distance penalty and analyzing the resulting TER‑vs‑λ curve and confidence‑based constraint ordering, the authors identify two failure modes—Binary Collapse and Confident Miscalibration—across multiple language pairs. The diagnostic also suggests practical next steps, such as applying a static constraint for immediate accuracy gains, and the authors release new English‑Sinhala and English‑Tamil APE datasets with accompanying code.
arXiv Computation and Language
Sep 2
Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Reranking
arXiv:2609.00588v1 Announce Type: new Abstract: Reranking methods, such as Minimum Bayes Risk (MBR) decoding and Quality Estimation (QE) reranking, are widely used in modern neural machine translatio...