A 3D Geometry Model of Vocal Tract Based on Smart Internet of Things

The Internet of Things(IoT)plays an essential role in the current and future generations of information,network,and communication development and applications.This research focuses on vocal tract visualization and modeling,which are critical issues in realizing inner vocal tract animation.That is applied in many fields,such as speech training,speech therapy,speech analysis and other speech production-related applications.This work constructed a geometric model by observation of Magnetic Resonance Imaging data,providing a new method to annotate and construct 3D vocal tract organs.The proposed method has two advantages compared with previous methods.Firstly it has a uniform construction protocol for all speech organs.Secondly,this method can build correspondent feature points between different speech organs.There are less than three control parameters can be used to describe every speech organ accurately,for which the accumulated contribution rate is more than 88%.By means of the reconfiguration,the model error is less than 1.0 mm.Regarding to the data from Chinese Magnetic resonance imaging(MRI),this is the first work of 3D vocal tract model.It will promote the theoretical research and development of the intelligent Internet of Things facing speech generation-related issues.

Research on Tibetan Speech Recognition Based on the Am-do Dialect

In China,Tibetan is usually divided into three major dialects:the Am-do,Khams and Lhasa dialects.The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan.Although this dialect has its own specific historical and social conditions and development,there have been different degrees of communication with other ethnic groups,but all the abovementioned dialects developed from the same language:Tibetan.This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language,which optimizes the problems existing in previous research.Audio data of the Am-do dialect are expanded by data augmentation technology combining noise and reverberation,and the morphological characteristics and characteristics of the Tibetan language are further considered.According to the particularity of Tibetan grammar,grammatical features are used to optimize grammatical relationships and are combined with a language model,and the Am-do dialect is scored and rescored.Experimental results show that compared with the baseline,our proposed new lexicon and data augmentation technology yields a relative increase of approximately 3%in character error rates(CERs)and a relative increase of 3%-19%in the recognition rate of acoustic models and language models.

Speech Encryption with Fractional Watermark

Research on the feature of speech and image signals are carried out from two perspectives,the time domain and the frequency domain.The speech and image signals are a non-stationary signal,so FT is not used for the non-stationary characteristics of the signal.When short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform(DFT).The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency domain.It has the problem of adjusting window size to a for desired resolution.But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities.This paper performs global processing speech encryption by combining speech with image of Fractional Fourier Transform.The speech signal is embedded watermark image that is processed by fractional transformation,and the embedded watermark has the effect of rotation and superposition,which improves the security of the speech.The paper results show that the proposed speech encryption method has a higher security level by Fractional Fourier Transform.The technology is easy to extend to practical applications.

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.