The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. Glottal flow waveform directly reflects the state of glottal excitation. Extracting acoustic features from glottal source signals may contribute to the detection of pathological voice. To improve the performance of voice pathology detection, this article investigates the contribution of the glottal flow waveform for pathological voice detection by evaluating the classification result using features extracted from raw speech utterances and corresponding glottal flow waveforms. The individual feature sets used are extracted from raw or glottal voice utterances with identical parameter settings, which are openSMILE acoustic features, audio features computed by Moving Picture Experts Group-7 standard and classical glottal source features. In addition, a feature selection method in terms of the wrapper approach is used to combine the single features ranked by using the Fisher discrimination ratio. Voice pathology detection experiments were carried out using Random Forest. The best accuracies of 88.52% for the Saarbrücken Voice database and 100.00% for the Massachusetts Eye and Ear Infirmary database are achieved using the combined feature set extracted from the glottal source signal, with improvement of 0.44-3.13% in the accuracies obtained by using raw speech utterances. Compared to state-of-the-art methods, the proposed method achieves the highest accuracy for the Massachusetts Eye and Ear Infirmary database and an increase of 2.75-17.16% in detection accuracy compared to other conventional pipeline systems for the Saarbrücken Voice databse. The experimental results demonstrate that using glottal flow waveform as source signal can improve the performance of pathological voice detection.

In recent years, more and more people suffer from voice-related diseases. Given the limitations of current pathological speech conversion methods, that is, a method can only convert a single kind of pathological voice. In this study, we propose a novel Encoder-Decoder Generative Adversarial Network (E-DGAN) to generate personalized speech for pathological to normal voice conversion, which is suitable for multiple kinds of pathological voices. Our proposed method can also solve the problem of improving the intelligibility and personalizing custom speech of pathological voices. Feature extraction is performed using a mel filter bank. The conversion network is an encoder-decoder structure, which is used to convert the mel spectrogram of pathological voices to the mel spectrogram of normal voices. After being converted by the residual conversion network, the personalized normal speech is synthesized by the neural vocoder. In addition, we propose a subjective evaluation metric named “content similarity” to evaluate the consistency between the converted pathological voice content and the reference content. The Saarbrücken Voice Database (SVD) is used to verify the proposed method. The intelligibility and content similarity of pathological voices are increased by 18.67% and 2.60%, respectively. Besides, an intuitive analysis based on a spectrogram was done and a significant improvement was achieved. The results show that our proposed method can improve the intelligibility of pathological voices and personalize the conversion of pathological voices into the normal voices of 20 different speakers. Our proposed method is compared with five other pathological voice conversion methods, and our proposed method has the best evaluation results.

Emotion shapes all aspects of our interpersonal and intellectual experiences. Its automatic analysis has therefore many applications. In this paper, we propose an emotional tonal speech dataset, Mandarin Chinese Emotional Speech Dataset - Portrayed (MES-P), with both distal and proximal labels. In contrast with state of the art datasets which only focused on perceived emotions, MES-P includes not only perceived emotions (proximal labels) but also intended emotions (distal labels), to make it possible to study human emotional intelligence, i.e. emotion expression/understanding ability, and emotional misunderstandings in real life. Furthermore, MES-P also captures a main feature of tonal languages, and provides emotional speech samples matching the tonal distribution in real life Mandarin. MES-P dataset also features emotion intensity variations, by introducing both moderate and intense versions for joy, anger, and sadness, in addition to neutral. Ratings of the collected speech samples are made in valence-arousal space through continuous coordinate locations, resulting in an emotional distribution pattern in 2D VA space. High consistency between the speakers' emotional intentions and the listeners' perceptions is also proved by Cohen's Kappa coefficients. Finally, extensive experiments are carried out as a baseline on MES-P for automatic emotion recognition and with comparison to human emotion intelligence.

Following the seminal work by Dubois et al. (Nat. Commun. 8, 14871 (2017)), we study a double-zero-index acoustic metamaterial with triangular lattice. By varying the height and diameter of air scatterers inside a parallel-plate acoustic waveguide, acoustic dispersion of the first-order waveguide mode can be manipulated and various interesting properties are explored. With accidental degeneracy of monopolar and dipolar modes, we numerically prove the double-zero-index properties of this novel acoustic metamaterial. Acoustic waveguides with tunable and asymmetric transmission are realized with this double-zero-index acoustic metamaterial embedded. Band inversion occurs if the bulk acoustic band diagram of this acoustic metamaterial is tuned. Deterministic interface states are found to exist on the interface between two acoustic metamaterials with inverted band diagrams.