Measuring benchmark optimization in speech recognition

Towards Quantifying Benchmark Optimization in ASR Models

arXiv:2608. 19936v1 Announce Type: cross Abstract: Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities.

TTS Arena: Benchmarking Text-to-Speech Models in the Wild

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems

arXiv:2607. 14846v1 Announce Type: cross Abstract: Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation.

Performance and Complexity Trade-off Optimization of Speech Models During Training

The paper introduces a reparameterization technique that injects feature noise to jointly optimize speech model performance and computational complexity during training. Unlike traditional pruning, this method dynamically adjusts model size for a desired performance‑complexity trade‑off without heuristic weight removal. The authors validate their approach with a synthetic example and two real‑world applications—voice activity detection and audio anti‑spoofing—providing publicly available code for further research.

Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition

arXiv:2605. 13087v2 Announce Type: replace-cross Abstract: Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance.

Benchmarking Automatic Speech Recognition Tools for Iberian Languages

arXiv:2609.36920v1 Announce Type: new Abstract: Comprehensive evaluations of automatic speech recognition (ASR) for Iberian languages remain limited, and low-resource languages, biases, and efficienc...