InQSS: a speech intelligibility and quality assessment model using a multi-task learning network

2021/11/04 by Yu‐Wen Chen, Chen, Yu-Wen, Yu Tsao +1 · 7 citations
Computer Science · Psychology · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Phonetics and Phonology Research #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.02585

openalex publication_date 2021/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Speech intelligibility and quality assessment models are essential tools for researchers to evaluate and improve speech processing models. However, only a few studies have investigated multi-task models for intelligibility and quality assessment due to the limitations of available data. In this study, we released TMHINT-QI, the first Chinese speech dataset that records the quality and intelligibility scores of clean, noisy, and enhanced utterances. Then, we propose InQSS, a non-intrusive multi-task learning framework for intelligibility and quality assessment. We evaluated the InQSS on both the training-from-scratch and the pretrained models. The experimental results confirm the effectiveness of the InQSS framework. In addition, the resulting model can predict not only the intelligibility scores but also the quality scores of a speech signal.

Cited by

• STSM-FiLM: A FiLM-Conditioned Neural Architecture for Time-Scale Modification of Speech
• From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modeling
• Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges
• Speech Intelligibility Assessment with Uncertainty-Aware Whisper Embeddings and sLSTM
• EffortNet: A Deep Learning Framework for Objective Assessment of Speech Enhancement Technologies Using EEG-Based Alpha Oscillations
• Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment
• Advancing Speech Quality Assessment Through Scientific Challenges and Open-source Activities

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