Alloxysta turcica Tataroğlu & Katılmış, 2023, a species recently described from Turkey, is now being synonymyzed with A. minuscula Andrews, 1978. This decision is based on morphological analysis, which has revealed significant similarities between the two species. The reasons and supporting illustrations for this synonymy are provided.

Introduction: The purpose of this study is to quantify the voice disorders in children with cochlear implantation and hearing aids. Until now, quantifying voice disorders has been done subjectively by speech experts and it is for the first time that the preset study tends to run an objective experiment  using signal processing features. Materials and Methods: 4 levels were considered to be qualify speech. Linear and nonlinear features were extracted from 5 Farsi words: “mashin”, “mar”, “moosh”, “gav” and “mowz” uttered by 30 subjects and then put into hidden Markov classifiers. Classifier outputs then were fused together to have better accuracy. The main hypothesis of the study is to answer this question: Can we separate children into 4 levels based on their voice features? Voice features including “fundamental frequency, first formant, second formant, third formant, first to second formant ratio, third to second formant ratio, Rational Intensity, nasality, approximate entropy and fractal dimension were extracted from speech segments and then are were given to artificial decision making systems (classifiers).Results: The results show that classifiers can separate 4 levels of voice disorders with the accuracy of 93.1%. Among the introduced features, first to second formant ratio and third to second formant ratio can be used directly to track voice recovery after using cochlear implantation or hearing aid.Conclusion: The output of this research study can act as a speaker independent system to help speech specialists with evaluating voice disorder recovery in children who fall in the same range of age.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

The conventional view on the problem of robustness in speech recognition is that performance degradation in ASR systems is due to mismatch between training and test conditions. If problem of robustness in ASR systems is considered as a mismatch between the training and testing conditions the solution would be to find a way to reduce it. Common approaches are: Data-Driven methods such as speech signal enhancement and using robust features and model-based methods that alter or adapt model of speech signal. In this paper, we study a model of environment and obtain a relation between noisy and clean speech features based on this model. We propose two techniques for mapping noisy features to clean features in cepstrum domain. We implement the proposed methods and some of precedent data-driven methods such as: spectral subtraction, cepstral mean normalization, cepstral mean and variance mean normalization and SNR-dependent cepstral normalization .We show that proposed methods outperform precedent methods and are effective for robust speech recognition in noisy environments.