Combination of dynamic features with a new mask to optimize neural network speech enhancement

Concerning the problem that the Neural Network speech enhancement algorithm cannot fully represent the nonlinear structure of speech due to feature selection,which leads to speech distortion.This paper proposes the combination of dynamic features with a new mask to optimize neural network speech enhancement.First,three features of noisy speech are extracted and spliced to obtain static features.Then,the first and second difference derivatives are obtained to capture the instantaneous signals of speech and fuse them into dynamic features.The combination of dynamic and static features completes internal complementarity of features and reduced speech distortion.Second,in order to enhance the intelligibility and clarity of speech at the same time,an adaptive mask is proposed,which can adjust the energy ratio of speech and noise as well as the ratio of the traditional mask and the square root mask.The Gammatone channel weight is used to modify the mask value in each channel to simulate the human auditory system and further improve the speech intelligibility.Finally,the simulation of multiple voices under different noise backgrounds shows that compared with different literature algorithms,the algorithm has a higher SNR,subjective speech quality and short-term objective intelligibility,which verifies the effectiveness of the algorithm.     

Tibetan Multi-Dialect Speech and Dialect Identity Recognition

Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single model. The experimental results show our method can simultaneously recognize speech content for different Tibetan dialects and identify the dialect with high accuracy using a unified model. The dialect information used in output for training can improve multi-dialect speech recognition accuracy, and the low-resource dialects got higher speech content recognition rate and dialect classification accuracy by multi-dialect and multi-task recognition model than task-specific models.     

针对多种处理痕迹的数字语音取证算法

现有的数字语音取证研究主要集中于对单一的某种操作进行检测，无法对不相关的操作进行判断。针对该问题，提出了一种能够同时检测经过变调、低通滤波、高通滤波和加噪这四种操作的数字语音取证方法。首先，计算语音的归一化梅尔频率倒谱系数（MFCC）统计矩特征；然后通过多个二分类器对特征进行训练，并组合投票得到多分类器；最后使用该多分类器对待测语音进行分类。在TIMIT以及UME语音库上的实验结果表明，归一化MFCC统计矩特征在库内实验中均达到了97%以上的检测率，且在对MP3压缩鲁棒性测试的实验中，检测率仍能保持在96%以上。     

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self-powered sensors, machine learning algorithms, and speaker recognition.     

Reasons for the Importance of Politeness

礼貌在大多数人看来，意味着好的礼节，得体的言谈举止。实际上礼貌所涉及的问题非常深奥、复杂。本文在分析礼貌内涵的基础上，从面子需求以及文化观念二方面详细阐述了礼貌用语重要性的原因。     

Study on Unequal Error Protection for Distributed Speech Recognition System

At present ,the trend to ever-increasing use ofdata communication is spreading to the mobile wire-less world. The small portable devices will be used toaccess these data and cry out for i mproved user inter-faces using speechinput , whichis very i mportan…     

Data-Driven Temporal Filtering on Teager Energy Time Trajectory for Robust Speech Recognition

Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include： principle component analysis （PCA） based filters, linear discriminant analysis （LDA） based filters and minimum classification error （MCE） based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms.     

Design and ARM‐embedded implementation of a chaotic map‐based multicast scheme for multiuser speech wireless communication

This paper proposes a chaotic map‐based multicast scheme for multiuser speech wireless communication and implements it in an ARM platform. The scheme compresses the digital audio signal decoded by a sound card and then encrypts it with a three‐level chaotic encryption scheme. First, the position of every bit of the compressed data is permuted randomly with a pseudo‐random number sequence (PRNS) generated by a 6‐D chaotic map. Then, the obtained data are further permuted in the level of byte with a PRNS generated by a 7‐D chaotic map. Finally, it is operated with a multiround chaotic stream cipher. The whole system owns the following merits: the redundancy in the original audio file is reduced effectively and the corresponding unicity distance is increased; the balancing point between a high security level of the system and real‐time conduction speed is achieved well. In the ARM implementation, the framework of communication of multicast–multiuser in a subnet and the Internet Group Manage Protocol is adopted to obtain the function of communication between one client and other ones. Comprehensive test results were provided to show the feasibility and security performance of the whole system. Copyright © 2016 John Wiley & Sons, Ltd.     

Wavelet-COH在通信语音质量评估算法中的应用研究

将小波变换用于子带分解,对纯净语音信号和受扰语音信号进行特征提取,包括质心、子带能量和带宽等,并进一步与一致性函数(COH)方法相结合对语音客观音质评价方法进行了研究,即Wavelet-COH方法。通过最小二乘多项式拟合模型,对Wavelet-COH方法得到的客观评测和主观评测结果进行相关分析,得出相应算法的相关系数和方差值。通过对比,表明Wavelet-COH语音评估方法比传统的COH客观评价方法有很大改善。     

一种改进的基于Viterbi的语音切分算法

主要针对文本提示型说话人识别中语音切分高精确度要求的问题,在利用Viterbi算法的语音切分基础上,提出了向后平滑搜索多帧能量极小值的语音切分方法。该算法首先对0~9的每个数字建立模型,然后利用Viterbi算法对随机数字串进行切分得到初始切分点,最后利用搜索多帧能量极小值的方法更新原始切分点。实验表明,相比于传统的切分算法,在误差范围小于20 ms之内,改进算法的切分准确率由82.1%提高到88%。