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

• URL: http://arxiv.org/abs/2111.02585v1
• Date: Thu, 4 Nov 2021 02:01:27 GMT
• Title: InQSS: a speech intelligibility assessment model using a multi-task learning network
• Authors: Yu-Wen Chen, Yu Tsao
• Abstract summary: In this study, we propose InQSS, a speech intelligibility assessment model that uses both spectrogram and scattering coefficients as input features. The resulting model can predict not only the intelligibility scores but also the quality scores of a speech.
• Score: 21.037410575414995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech intelligibility assessment models are essential tools for researchers to evaluate and improve speech processing models. In this study, we propose InQSS, a speech intelligibility assessment model that uses both spectrogram and scattering coefficients as input features. In addition, InQSS uses a multi-task learning network in which quality scores can guide the training of the speech intelligibility assessment. The resulting model can predict not only the intelligibility scores but also the quality scores of a speech. The experimental results confirm that the scattering coefficients and quality scores are informative for intelligibility. Moreover, we released TMHINT-QI, which is a Chinese speech dataset that records the quality and intelligibility scores of clean, noisy, and enhanced speech.

Related papers

• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech [50.95292368372455]
  We propose VQScore, a self-supervised metric for evaluating speech based on the quantization error of a vector-quantized-variational autoencoder (VQ-VAE) The training of VQ-VAE relies on clean speech; hence, large quantization errors can be expected when the speech is distorted. We found that the vector quantization mechanism could also be used for self-supervised speech enhancement (SE) model training.
  arXiv  Detail & Related papers  (2024-02-26T06:01:38Z)
• On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification [7.83105437734593]
  Self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data. This study assesses large-scale self-supervised models' performance in few-shot audio classification.
  arXiv  Detail & Related papers  (2024-02-02T10:00:51Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment [54.31355080688127]
  We introduce a text-prompted Semantic Affinity Quality Index (SAQI) and its localized version (SAQI-Local) using Contrastive Language-Image Pre-training (CLIP) BVQI-Local demonstrates unprecedented performance, surpassing existing zero-shot indices by at least 24% on all datasets. We conduct comprehensive analyses to investigate different quality concerns of distinct indices, demonstrating the effectiveness and rationality of our design.
  arXiv  Detail & Related papers  (2023-04-28T08:06:05Z)
• Visualising and Explaining Deep Learning Models for Speech Quality Prediction [0.0]
  The non-intrusive speech quality prediction model NISQA is analyzed in this paper. It is composed of a convolutional neural network (CNN) and a recurrent neural network (RNN)
  arXiv  Detail & Related papers  (2021-12-12T12:50:03Z)
• HASA-net: A non-intrusive hearing-aid speech assessment network [52.83357278948373]
  We propose a DNN-based hearing aid speech assessment network (HASA-Net) to predict speech quality and intelligibility scores simultaneously. To the best of our knowledge, HASA-Net is the first work to incorporate quality and intelligibility assessments utilizing a unified DNN-based non-intrusive model for hearing aids. Experimental results show that the predicted speech quality and intelligibility scores of HASA-Net are highly correlated to two well-known intrusive hearing-aid evaluation metrics.
  arXiv  Detail & Related papers  (2021-11-10T14:10:13Z)
• Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features [30.57631206882462]
  The MOSA-Net is designed to estimate speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input. We show that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (BLS) scores when tested on both noisy and enhanced speech utterances.
  arXiv  Detail & Related papers  (2021-11-03T17:30:43Z)
• Task-Specific Normalization for Continual Learning of Blind Image Quality Models [105.03239956378465]
  We present a simple yet effective continual learning method for blind image quality assessment (BIQA) The key step in our approach is to freeze all convolution filters of a pre-trained deep neural network (DNN) for an explicit promise of stability. We assign each new IQA dataset (i.e., task) a prediction head, and load the corresponding normalization parameters to produce a quality score. The final quality estimate is computed by black a weighted summation of predictions from all heads with a lightweight $K$-means gating mechanism.
  arXiv  Detail & Related papers  (2021-07-28T15:21:01Z)
• Deep Learning Based Assessment of Synthetic Speech Naturalness [14.463987018380468]
  We present a new objective prediction model for synthetic speech naturalness. It can be used to evaluate Text-To-Speech or Voice Conversion systems.
  arXiv  Detail & Related papers  (2021-04-23T16:05:20Z)

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.