#POLQA
metrics. Although there are many objective metrics like the Perceptual Evaluation of Speech Quality (PESQ), Perceptual Objective Listening Quality Assessment (POLQA) or Short-Time Objective Intelligibility (STOI) but none of them is feasible in [2/5 of https://arxiv.org/abs/2506.02082v1]
June 4, 2025 at 6:00 AM
SpeechQualityLLM: LLM-Based Multimodal Assessment of Speech Quality
Objective speech quality assessment is central to telephony, VoIP, and streaming systems, where large volumes of degraded audio must be monitored and optimized at scale. Classical metrics such as PESQ and POLQA approximate human mean opinion scores (MOS) but require carefully controlled conditions and expensive listening tests, while learning-based models such as NISQA regress MOS and multiple perceptual dimensions from waveforms or spectrograms, achieving high correlation with subjective ratings yet remaining rigid: they do not support interactive, natural-language queries and do not natively provide textual rationales. In this work, we introduce SpeechQualityLLM, a multimodal speech quality question-answering (QA) system that couples an audio encoder with a language model and is trained on the NISQA corpus using template-based question-answer pairs covering overall MOS and four perceptual dimensions (noisiness, coloration, discontinuity, and loudness) in both single-ended (degraded only) and double-ended (degraded plus clean reference) setups. Instead of directly regressing scores, our system is supervised to generate textual answers from which numeric predictions are parsed and evaluated with standard regression and ranking metrics; on held-out NISQA clips, the double-ended model attains a MOS mean absolute error (MAE) of 0.41 with Pearson correlation of 0.86, with competitive performance on dimension-wise tasks. Beyond these quantitative gains, it offers a flexible natural-language interface in which the language model acts as an audio quality expert: practitioners can query arbitrary aspects of degradations, prompt the model to emulate different listener profiles to capture human variability and produce diverse but plausible judgments rather than a single deterministic score, and thereby reduce reliance on large-scale crowdsourced tests and their monetary cost.
arxiv.org
December 10, 2025 at 5:29 AM
Voice comms for BVLOS just got a major boost: AURA, NPUASTS and the FAA proved its hybrid voice-relay in ND — avg latency <192ms, 99% <238ms, POLQA scores met FAA standards.

Big step for safe drone flights.
January 14, 2026 at 3:16 PM
📡 NTN Networks: Voice Quality Matters More Than Ever

As #NTNs move from trials to real deployments, voice communication is becoming a critical success factor — not just coverage.

👉 Learn more

https://ow.ly/cWcX50Y3Hw9
#Satellite #VoiceQuality #POLQA #VoNR #5G #LEOSatellites #NetworkTesting
Non-Terrestrial Network Testing | SmartViser
Discover the essentials of Non-Terrestrial Network (NTN) testing, from latency and signal quality to secure communications. Learn how SmartViser’s viSer test automation suite ensures reliable and efficient NTN performance for seamless global connectivity
ow.ly
January 26, 2026 at 12:30 PM
🔊 Why does voice quality still matter in today’s data-driven mobile world?

In our latest blog, we explore: 📡 The role of voice in modern and Non-Terrestrial Networks

🔗 Read the full article:

👉 www.smartviser.com/post/why-voi...
#VoiceQuality #VoLTE #NetworkTesting #VoiceCalls #POLQA #NTN #5G
Why Voice Quality Still Matters: A Critical Differentiator in Modern Mobile Networks
Discover why Voice Quality is crucial in modern mobile networks. Learn how superior Voice Quality enhances user experience and brand loyalty
www.smartviser.com
April 3, 2025 at 3:44 PM
Piotr Rybak, Piotr Przyby{\l}a, Maciej Ogrodniczuk
PolQA: Polish Question Answering Dataset
https://arxiv.org/abs/2212.08897
February 23, 2024 at 8:06 AM