一种小波域音频信息隐藏方法

提出了一种基于量化的小波域音频隐藏算法，将保密语音隐藏到载体音频中．为提高隐藏重和保密语音传输的安全性，对保密语音进行了小波域压缩编码和m序列的扩频调制，生成待隐藏的比特序列；通过量化方法，将编码和调制后的保密语音隐藏到载体音频的小波系数中；保密语音的恢复过程不需要使用原始音频、仿真结果表明，隐藏有保密语音的载体音频听觉质量没有明显下降，提取的保密语音感知质量较好；该算法对重量化、加噪、低通滤波等攻击均有良好的鲁棒性．     

Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features

Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by human-centric glimpse-based psychological research into facial barcodes, and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor-based image patches), can successfully be used for speech recognition with LSTM-based machine learning. This approach can successfully extract low dimensionality lip parameters with a minimum of processing. One key difference between using these Gabor-based features and using other features such as traditional DCT, or the current fashion for CNN features is that these are human-centric features that can be visualised and analysed by humans. This means that it is easier to explain and visualise the results. They can also be used for reliable speech recognition, as demonstrated using the Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate of over 82%, which compares well to less explainable features in the literature.     

Reliability of Speaking and Maximum Voice Range Measures in Screening for Dysphonia

Speech range profile (SRP) is a graphical display of frequency-intensity occurring interactions during functional speech activity. Few studies have suggested the potential clinical applications of SRP. However, these studies are limited to qualitative case comparisons and vocally healthy participants. The present study aimed to examine the effects of voice disorders on speaking and maximum voice ranges in a group of vocally untrained women. It also aimed to examine whether voice limit measures derived from SRP were as sensitive as those derived from voice range profile (VRP) in distinguishing dysphonic from healthy voices. Ninety dysphonic women with laryngeal pathologies and 35 women with normal voices, who served as controls, participated in this study. Each subject recorded a VRP for her physiological vocal limits. In addition, each subject read aloud the "North Wind and the Sun" passage to record SRP. All the recordings were captured and analyzed by Soundswell's computerized real-time phonetogram Phog 1.0 (Hitech Development AB, T?by, Sweden). The SRPs and the VRPs were compared between the two groups of subjects. Univariate analysis results demonstrated that individual SRP measures were less sensitive than the corresponding VRP measures in discriminating dysphonic from normal voices. However, stepwise logistic regression analyses revealed that the combination of only two SRP measures was almost as effective as a combination of three VRP measures in predicting the presence of dysphonia (overall prediction accuracy: 93.6% for SRP vs 96.0% for VRP). These results suggest that in a busy clinic where quick voice screening results are desirable, SRP can be an acceptable alternate procedure to VRP.     

Breathing muscle activity and subglottal pressure dynamics in singing and speech

Previous studies have indicated that the diaphragm may contribute to the accomplishment of the quick and precise subglottal pressure changes required during singing. The present investigation compares data collected from simultaneously recorded electromyograms from breathing muscles and transdiaphragmatic pressure during singing as well as during nonsense and emphatic speech.     

The speaker''s formant in male voices

Spectral analysis of vowels during connected speech can be performed using the spectral intensity distribution within critical bands corresponding to a natural scale on the basilar membrane. Normalization of the spectra provides the opportunity to make objective comparisons independent from the recording level. An increasing envelope peak between 3,150 and 3,700 Hz has been confirmed statistically for a combination of seven vowels in three groups of male speakers with hoarse, normal, and professional voices. Each vowel is also analyzed individually. The local energy maximum is called “the speaker's formant” and can be found in the region of the fourth formant. The steepness of the spectral slope (i.e. the rate of decline) becomes less pronounced when the sonority or the intensity of the voice increases. The speaker's formant is connected with the sonorous quality of the voice. It increases gradually and is approximately 10 dB higher in professional male voices than in normal male voices at neutral loudness (60 dB at 0.3 min). The peak intensity becomes stronger (30 dB above normal voices) when the overall speaking loudness is increased to 80 dB. Shouting increases the spectral energy of the adjacent critical bands but not the speaker's formant itself.     

第四讲语音信号处理的现状和展望

文章简要介绍了“语音信号处理”这一分支学科形成和发展的历史过程．指出了它在现代信息科学技术中的地位和作用．介绍了语音信号处理在应用领域的一些重要课题，如语音的低速率编码，语音的规则合成和文一语转换系统，语音识别和人一机语音对话等，这些仍然是当前研究的热点．文章最后展望了语音信号处理的发展前景，指出在这个领域还有很多难题等待人们去研究探索．     

Att-U-Net：融合注意力机制的U-Net骨导语声增强*

近年来大量全卷积网络、U-Net等编解码网络结构应用于语音增强,它们具有计算复杂度低、模型参数少等优势。然而,与长短时记忆模型等方法相比,这些编解码结构仍存在不能充分利用先后时间之间和高低频率之间的关联信息等缺点,尤其对于长序列数据的输入,编解码结构存在信息丢失的问题。为保持计算效率的同时考虑更充分的时频关联信息建模,本文提出一种融合注意力机制的U-Net网络的骨导语音增强方法（Att-U-Net）,通过在跳跃连接中引入注意力机制,生成一个权重矩阵,将编码层中的全局信息根据权重融入对应的解码层中,使网络在编解码过程中能够关注输入数据中与增强目标相关程度高的重要信息,同时抑制不相关的信息。在骨导语音数据集上的实验表明,融合注意力机制的U-Net网络能在保持模型轻量化的同时有效提升骨导语音的增强效果,增强后的语音在各项客观评价指标上均优于基线模型。通过对编解码网络中间层的可视化分析发现,在解码过程中注意力机制有效地保留了有声段的信息,滤除了骨导语音由于骨导传声特性带来的中频共振,从而使得增强后的骨导语音具有较好的听觉效果。