English speaking proficiency assessment using speech and electroencephalography signals

In this paper, the English speaking proficiency level of non-native English speakerswas automatically estimated as high, medium, or low performance. For this purpose, the speech of 142 non-native English speakers was recorded and electroencephalography (EEG) signals of 58 of them were recorded while speaking in English. Two systems were proposed for estimating the English proficiency level of the speaker; one used 72 audio features, extracted from speech signals, and the other used 112 features extracted from EEG signals. Multi-class support vector machines (SVM) was used for training and testing both systems using a cross-validation strategy. The speech-based system outperformed the EEG system with 68% accuracy on 60 testing audio recordings, compared with 56% accuracy on 30 testing EEG recordings

Acoustic model selection using limited data for accent robust speech recognition

This paper investigates techniques to compensate for the effects of regional accents of British English on automatic speech recognition (ASR) performance. Given a small amount of speech from a new speaker, is it better to apply speaker adaptation, or to use accent identification (AID) to identify the speaker’s accent followed by accent-dependent ASR? Three approaches to accent-dependent modelling are investigated: using the ‘correct’ accent model, choosing a model using supervised (ACCDIST-based) accent identifi- cation (AID), and building a model using data from neighbouring speakers in ‘AID space’. All of the methods outperform the accentindependent model, with relative reductions in ASR error rate of up to 44%. Using on average 43s of speech to identify an appropriate accent-dependent model outperforms using it for supervised speaker-adaptation, by 7%

Self-admitted technical debt classification using natural language processing word embeddings

Recent studies show that it is possible to detect technical dept automatically from source code comments intentionally created by developers, a phenomenon known as self-admitted technical debt. This study proposes a system by which a comment or commit is classified as one of five dept types, namely, requirement, design, defect, test, and documentation. In addition to the traditional term frequency-inverse document frequency (TF-IDF), several word embeddings methods produced by different pre-trained language models were used for feature extraction, such as Word2Vec, GolVe, bidirectional encoder representations from transformers (BERT), and FastText. The generated features were used to train a set of classifiers including Naive Bayes (NB), random forest (RF), support vector machines (SVM), and two configurations of convolutional neural network (CNN). Two datasets were used to train and test the proposed systems. Our collected dataset (A-dataset) includes a total of 1,513 comments and commits manually labeled. Additionally, a dataset, consisting of 4,071 labeled comments, used in previous studies (M-dataset) was also used in this study. The RF classifier achieved an accuracy of 0.822 with A-dataset and 0.820 with the M-dataset. CNN with A-dataset achieved an accuracy of 0.838 using BERT features. With Mdataset, the CNN achieves an accuracy of 0.809 and 0.812 with BERT and Word2Vec, respectively

Contrasting the Effects of Different Frequency Bands on Speaker and Accent Identification

Safavi,Saed: Russell,Martin ,: Jancovick,Peter: Carey,Michael:This letter presents an experimental study investigating the effect of frequency sub-bands on regional accent identification (AID) and speaker identification (SID) performance on the ABI-1 corpus. The AID and SID systems are based on Gaussian mixture modeling. The SID experiments show up to 100% accuracy when using the full 11.025 kHz bandwidth. The best AID performance of 60.34% is obtained when using band-pass filtered (0.23–3.4 kHz) speech. The experiments using isolated narrow sub-bands show that the regions (0–0.77 kHz) and (3.40–11.02 kHz) are the most useful for SID, while those in the region (0.34–3.44 kHz) are best for AID. AID experiments are also performed with intersession variability compensation, which provides the biggest performance gain in the (2.23–5.25 kHz) regio

Microfluidic Flow Synthesis of Functional Mesoporous Silica Nanofibers with Tunable Aspect Ratios

Microfluidic techniques open new frontiers for the controllable synthesis of functional micro/nanomaterials with desired shapes for a variety of applications. In this study, miniaturized spiral-shaped microchannel with two inlets and one outlet was specially designed for the controllable flow synthesis of mesoporous silica nanofibers (MSNFs) using one inlet flow containing cetyltrimethylammonium bromide and diluted ammonia and the other inlet flow containing diluted tetraethyl orthosilicate. The aspect ratios and diameters of MSNFs can be easily tuned by changing the flow rates and/or the concentrations of reactants. In addition, fluorescent dyes, magnetic nanoparticles, therapeutic drugs, or silver nanoparticles can be simultaneously assembled into MSNFs to make them promising functional materials in bioimaging, theranostics, and catalysis fields