Effectiveness of ideal ratio mask for non-intrusive quality assessment of noise suppressed speech

Soni, Meet H.; Patil, Hemant A.

doi:10.23919/eusipco.2017.8081272

Cited by 7 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…D. -S. Kim et al [14] proposed a perceptually motivated algorithm based on a temporal envelope representation of speech to assess speech quality. Meet H. Soni [15] used the ideal ratio mask (IRM) for non-intrusive quality assessment of noise suppressed speech. Wang [16] applied autoencoder to extract bottleneck features of speech signals and mapped the features to the predicted MOS using support vector regression (SVR) [17].…”

Section: Related Workmentioning

confidence: 99%

Neural network-based non-intrusive speech quality assessment using attention pooling function

Liu

Wang

et al. 2021

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

Recently, the non-intrusive speech quality assessment method has attracted a lot of attention since it does not require the original reference signals. At the same time, neural networks began to be applied to speech quality assessment and achieved good performance. To improve the performance of non-intrusive speech quality assessment, this paper proposes a neural network-based assessment method using attention pooling function. The proposed systems are based on the convolutional neural networks (CNNs), bidirectional long short-term memory (BLSTM), and CNN-LSTM structure. Comparing four types of pooling functions both theoretically and experimentally, we find the attention pooling function performs the best among the four. Experiments are conducted in a dataset containing various degraded speech signals with corresponding subjective quality scores. The results show that the proposed CNN-LSTM model using attention pooling function achieves state-of-the-art correlation coefficient (R) and root-mean-square error (RMSE) of 0.967 and 0.269, outperforming the performance of standardization ITU-T P.563 and autoencoder-support vector regression method.

show abstract

Section: Related Workmentioning

confidence: 99%

Neural network-based non-intrusive speech quality assessment using attention pooling function

Liu

Wang

et al. 2021

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

“…As will be explained in Chapter 4, in supervised learning terminology, non-intrusive quality estimation can be described as a multi-class classification or a regression problem, where the input and output are the signal features and the quality score respectively [1]. Several non-intrusive methods have recently been proposed in the context of quality assessment, using machine learning algorithms for estimating the score of audio signals [23,24,25,26,27,28,29,30,31]. Figure (1.1) shows the high-level structure of a non-intrusive quality assessment system.…”

Section: Approaches and Principlesmentioning

confidence: 99%

“…These systems may differ in the considered features, the predicting function, or both. The work de-scribed in [60] makes use of a classifier to predict the discrete value of quality score while the works described in [23,24,25,26,27,28,29,31,61,62] apply regression methods (with shallow architectures) to estimate the subjective Mean Opinion Score (MOS) assigned to a speech file. On the other hand, approaches in [16,30] use a combination of classification and regression algorithms as the predicting function.…”

Section: Relevant Workmentioning

confidence: 99%

“…Recently, many quality estimation systems have been introduced based on machine learning methods [28,29,30,31]. Our work is distinct from them in two significant aspects: we mimic the behaviour of the human brain that is not trained with degraded signals.…”

Section: Related Workmentioning

confidence: 99%

“…In conversational tests, two people are usually questioned about the quality aspects of the conversation after they had a conversation over the system under the test. However, because of the complexities involved in conversational tests, the most frequently measured quantity is the listening quality [30,31,42,43]. In the listening context, which is the focus of this thesis, the speech quality is mainly affected by speech distortion due to speech codecs, background noise, and packet loss, whereas in the conversations, the impact of other degradations such as talker echo and path delay must also be considered.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Non-Intrusive Speech Quality Assessment

Hakami¹

View full text Add to dashboard Cite

This thesis presents two studies on non-intrusive speech quality assessment methods. The first applies supervised learning methods to speech quality assessment, which is a common approach in machine learning based quality assessment. To outperform existing methods, we concentrate on enhancing the feature set. In the second study, we analyse quality assessment from a different point of view inspired by the biological brain and present the first unsupervised learning based non-intrusive quality assessment that removes the need for labelled training data. Supervised learning based, non-intrusive quality predictors generally involve the development of a regressor that maps signal features to a representation of perceived quality. The performance of the predictor largely depends on 1) how sensitive the features are to the different types of distortion, and 2) how well the model learns the relation between the features and the quality score. We improve the performance of the quality estimation by enhancing the feature set and using a contemporary machine learning model that fits this objective. We propose an augmented feature set that includes raw features that are presumably redundant. The speech quality assessment system benefits from this redundancy as it results in reducing the impact of unwanted noise in the input. Feature set augmentation generally leads to the inclusion of features that have non-smooth distributions. We introduce a new pre-processing method and re-distribute the features to facilitate the training. The evaluation of the system on the ITU-T Supplement23 database illustrates that the proposed system outperforms the popular standards and contemporary methods in the literature. The unsupervised learning quality assessment approach presented in this thesis is based on a model that is learnt from clean speech signals. Consequently, it does not need to learn the statistics of any corruption that exists in the degraded speech signals and is trained only with unlabelled clean speech samples. The quality has a new definition, which is based on the divergence between 1) the distribution of the spectrograms of test signals, and 2) the pre-existing model that represents the distribution of the spectrograms of good quality speech. The distribution of the spectrogram of the speech is complex, and hence comparing them is not trivial. To tackle this problem, we propose to map the spectrograms of speech signals to a simple latent space. Generative models that map simple latent distributions into complex distributions are excellent platforms for our work. Generative models that are trained on the spectrograms of clean speech signals learned to map the latent variable $Z$ from a simple distribution $P_Z$ into a spectrogram $X$ from the distribution of good quality speech. Consequently, an inference model is developed by inverting the pre-trained generator, which maps spectrograms of the signal under the test, $X_t$, into its relevant latent variable, $Z_t$, in the latent space. We postulate the divergence between the distribution of the latent variable and the prior distribution $P_Z$ is a good measure of the quality of speech. Generative adversarial nets (GAN) are an effective training method and work well in this application. The proposed system is a novel application for a GAN. The experimental results with the TIMIT and NOIZEUS databases show that the proposed measure correlates positively with the objective quality scores.

show abstract

Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features

Zezario

Chen

et al. 2023

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Non-intrusive speech assessment metrics have garnered significant attention in recent years, and several deep learning-based models have been developed accordingly. Although these models are more flexible than conventional speech assessment metrics, most of them are designed to estimate a specific evaluation score, whereas speech assessment generally involves multiple facets. Herein, we propose a cross-domain multiobjective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. More specifically, MOSA-Net is designed to estimate the speech quality, intelligibility, and distortion assessment scores of an input test speech signal. It comprises a convolutional neural network and bidirectional long short-term memory (CRNN) architecture for representation extraction, and a multiplicative attention layer and a fully connected layer for each assessment metric. In addition, cross-domain features (spectral and timedomain features) and latent representations from self-supervised learned (SSL) models are used as inputs to combine rich acoustic information from different speech representations to obtain more accurate assessments. Experimental results show that MOSA-Net can improve the linear correlation coefficient (LCC) by 0.026 (0.990 vs 0.964 in seen noise environments) and 0.012 (0.969 vs 0.957 in unseen noise environments) in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-task model for PESQ prediction, and improve LCC by 0.021 (0.985 vs 0.964 in seen noise environments) and 0.047 (0.836 vs 0.789 in unseen noise environments) in short-time objective intelligibility (STOI) prediction, compared to STOI-Net (based on CRNN), an existing single-task model for STOI prediction. Moreover, MOSA-Net, originally trained to assess objective scores, can be used as a pre-trained model to be effectively adapted to an assessment model for predicting subjective quality and intelligibility scores with a limited amount of training data. Experimental results show that MOSA-Net can improve LCC by 0.018 (0.805 vs 0.787) in mean opinion score (MOS) prediction, compared to MOS-SSL, a strong singletask model for MOS prediction. In light of the confirmed prediction capability, we further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and

show abstract

Effectiveness of ideal ratio mask for non-intrusive quality assessment of noise suppressed speech

Cited by 7 publications

References 18 publications

Neural network-based non-intrusive speech quality assessment using attention pooling function

Neural network-based non-intrusive speech quality assessment using attention pooling function

Machine Learning for Non-Intrusive Speech Quality Assessment

Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features

Contact Info

Product

Resources

About