A Robust, Real-Time Voice Activity Detection Algorithm for Embedded Mobile Devices

When an Automatic Speech Recognition (ASR) system is applied in noisy environments, Voice Activity Detection (VAD) is crucial to the performance of the overall system. The employment of the VAD for ASR on embedded mobile systems will minimize physical distractions and make the system convenient to use. Conventional VAD algorithm is of high complexity, which makes it unsuitable for embedded mobile devices; or of low robustness, which holds back its application in mobile noisy environments. In this paper, we propose a robust VAD algorithm specifically designed for ASR on embedded mobile devices. The architecture of the proposed algorithm is based on a two-level decision making strategy, where there is an interaction between a lower features-based level and subsequent decision logic based on a finite-state machine. Many discriminating features are employed in the lower level to improve the robustness of the VAD. The two-level decision strategy allows different features to be used in different states and reduces the cost of the algorithm, which makes the proposed algorithm suitable for embedded mobile devices. The evaluation experiments show the proposed VAD algorithm is robust and contribute to the overall performance gain of the ASR system in various acoustic environments.     

Boot Camp: A Novel Intensive Approach to Voice Therapy

It is well known in the disciplines of neurobiology, exercise physiology, motor learning, and psychotherapy that desirable learning and behavior changes occur primarily from practice that involves high-intensity overload, variability, and specificity of training. We propose a novel treatment approach called intensive short-term voice therapy that uses these practice parameters for recalcitrant dysphonia. Intensive short-term voice therapy involves multiple sessions with a variety of clinicians, incorporating multiple simultaneous therapeutic approaches. The intensive short-term voice therapy approach is characterized by voice therapy for 1–4 successive days each with an average of 5 hours of therapy and five clinicians. This form of intensive voice therapy provides rigorous practice, involving not only overload but also opportunities for specificity and individuality thereby facilitating better transfer of learned skills. This article discusses the conceptual, theoretical, and practical foundations of this novel therapy approach.     

G. Paul Moore lecture: Rational thought: The impact of voice science upon voice care

Recent evolution in scientific knowledge and technology has led to monumental improvement in the standard of care for patients with voice disorders. New concepts in anatomy, physiology, measurement, and analysis have provided voice care professionals with not merely better understanding, but moreover an extensive vocabulary with which to think about voice function and dysfunction. Previously, we had to depend too much upon anecdote and “the art of medicine.” Thanks to scientific advances, we now have the tools we need for rational thought about the human voice. This is the fundamental change responsible for recent great advances in voice care.     

基于视觉显著度的说话检测

现有基于视觉信息的说话检测方法中往往依赖预定参数或者阈值作为分类平面,鲁棒性差且泛化能力不强.针对这一问题,本文提出了一种基于显著度的视觉说话检测方法,利用像素的色彩信息和嘴唇的空间位置特性检测嘴唇,通过分析嘴唇运动和说话假设的关系,将图像能量作为特征,并结合经典的隐马尔科夫模型（hidden Markov models,HMM）和支持向量机（support vector machine,SVM）作为判决方法来进行检测.实验结果表明,本文提出的嘴唇检测方法正确率可达到92%.     

一种机器人智能语音识别算法研究

针对智能机器人在非特定人语音识别中识别率偏低的问题,提出了一种双门限的端点检测算法,精确地检测出了语音端点,对分形维数和Mel频率倒谱系数(MFCC)进行结合,同时基于隐马尔可夫(HMM)模型,提出了智能机器人命令识别系统;在实验室环境下,利用Cool Edit软件录制了5男5女的语音,采样率为8 kHz,精度为16位,内容为5个命令词,每个词均被采集6次,将每人的前3次发音作为模板语音,后3次发音作为测试语音,实验结果表明,系统识别率可以达到85%以上,MFCC与分形维数混合的语音特征参数的算法提高了系统识别率,优化了系统性能;该方法用于非特定人语音智能识别是可行的、有效的。     

数据增强和复杂特征优化的类不平衡病理嗓音检测

该文以提高病理嗓音多分类准确性为目标,构建了一种基于数据增强和复杂特征优化的类不平衡病理嗓音检测系统。首先,对32种声学特征进行分析并将其归类为时域类特征和频域类特征;其次,采用改进的合成少数类过采样技术对数据集进行增广与均衡处理;然后,结合高效相关性特征选择算法和盒图对多维声学特征进行融合优化,综合评估各特征的判别能力;最后,基于随机森林分类器,详细分析和验证不同特征组合的分类性能。结果表明,该文提出的融合优化特征集(To、Fatr、Jita、sAPQ、vAm、NHR)在随机森林分类器下,对声带小结、息肉、水肿及麻痹4种病理嗓音的分类性能表现优异,取得了88.6%的分类准确率、88.4%的召回率、88.4%的F1分数和99.7%的AUC值。     

基于Linux平台的电话语音邮件系统

通过 CTI（电脑语音 ）技术将电话语音与电子邮件相结合 ,提出并实现了一个基于 L inux平台的电话语音邮件系统 ,并对原理和各功能模块的实现方法进行了论述 .该系统通过 Internet和现今十分普及的电信网相连接 ,提供了一个全方位的交叉的通信平台 ,拓宽了 E- mail的应用领域