Design of Automatic Scoring System for Oral English Test Based on Sequence Matching and Big Data Analysis

With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking.

Speech Enhancement Using Deep Learning Methods: A Review

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.

Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions

Generative adversarial networks (GANs) have recently garnered significant attention for their use in speech enhancement tasks, in which they generally process and reconstruct speech waveforms directly. Existing GANs for speech enhancement rely solely on the convolution operation, which may not accurately characterize the local information of speech signals—particularly high-frequency components. Sinc convolution has been proposed in order to allow the GAN to learn more meaningful filters in the input layer, and has achieved remarkable success in several speech signal processing tasks. Nevertheless, Sinc convolution for speech enhancement is still an under-explored research direction. This paper proposes Sinc–SEGAN, a novel generative adversarial architecture for speech enhancement, which usefully merges two powerful paradigms: Sinc convolution and the speech enhancement GAN (SEGAN). There are two highlights of the proposed system. First, it works in an end-to-end manner, overcoming the distortion caused by imperfect phase estimation. Second, the system derives a customized filter bank, tuned for the desired application compactly and efficiently. We empirically study the influence of different configurations of Sinc convolution, including the placement of the Sinc convolution layer, length of input signals, number of Sinc filters, and kernel size of Sinc convolution. Moreover, we employ a set of data augmentation techniques in the time domain, which further improve the system performance and its generalization abilities. Compared to competitive baseline systems, Sinc–SEGAN overtakes all of them with drastically reduced system parameters, demonstrating its effectiveness for practical usage, e.g., hearing aid design and cochlear implants. Additionally, data augmentation methods further boost Sinc–SEGAN performance across classic objective evaluation criteria for speech enhancement.

Dimension Reduction Analysis of Vowel Signal Data Based on Manifold Learning

Data dimension reduction is an important method to overcome dimension disaster and obtain as much valuable information as possible. Speech signal is a kind of non-stationary random signal with high redundancy, and proper dimension reduction methods are needed to extract and analyze the signal features efficiently in speech signal processing. Studies have shown that manifold structure exists in high-dimensional data. Manifold dimension reduction method aiming at discovering the intrinsic geometric structure of data may be more effective in dealing with practical problems. This paper studies a data dimension reduction method based on manifold learning and applies it to the analysis of vowel signals.

Application Research of Speech Signal Processing Technology Based on Cloud Computing Platform

In recent years, in the context of the rapid development of information technology, artificial intelligence has also developed. People have begun to train machines. Many machines have been able to gradually understand human languagesand perform a series of actions based on language instructions. On this basis, scientific researchers hope that the machine can be more intelligent and humane. In the noise estimation stage, a noise estimation algorithm based on speech detection is used to effectively estimate the noise. Secondly, according to the characteristics of the method of speech noise reduction processing, a method of processing speech noise is realized. Finally, simulation experiments are used to illustrate the effectiveness of the algorithm. Aiming at the shortcomings of traditional speech noise reduction algorithms, improvements were made in adaptive filter estimation. The model's speech noise reduction algorithm was obtained. The cepstrum estimation of speech signals was modified, and the effect of speech enhancement was significantly improved.

Certain Applications of LabVIEW in the Field of Electronics and Communication

The LabVIEW platform with graphical programming environment, will help to integrate the human machine interface controller with the software like MATLAB, Python etc. This platform plays the vital role in many pioneering areas like speech signal processing, bio medical signals like Electrocardiogram (ECG) and Electroencephalogram (EEG) processing, fault analysis in analog electronic circuits, Cognitive Radio(CR), Software Defined Radio (SDR), flexible and wearable electronics. Nowadays most engineering colleges redesign their laboratory curricula for the students to enhance the potential inclusion of remote based laboratory to facilitate and encourage the students to access the laboratory anywhere and anytime. This would help every young learner to bolster their innovation, if the laboratory environment is within the reach of their hand. LabVIEW is widely recognized for its flexibility and adaptability. Due to the versatile nature of LabVIEW in the Input- Output systems, it has find its broad applications in integrated systems. It can provide a smart assistance to deaf and dumb people for interpreting the sign language by gesture recognition using flex sensors, monitor the health condition of elderly people by predicting the abnormalities in the heart beat through remote access, and identify the stage of breast cancer from the Computed tomography (CT) and Magnetic resonance imaging (MRI) scans using image processing techniques. In this chapter, the previous work of authors who have extensively incorporated LabVIEW in the field of electronics and communication are discussed in detail.