Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

Analysis of Deaf Speakers’ Speech Signal for Understanding the Acoustic Characteristics by Territory Specific Utterances

An important concern with the deaf community is inability to hear partially or totally. This may affect the development of language during childhood, which limits their habitual existence. Consequently to facilitate such deaf speakers through certain assistive mechanism, an effort has been taken to understand the acoustic characteristics of deaf speakers by evaluating the territory specific utterances. Speech signals are acquired from 32 normal and 32 deaf speakers by uttering ten Indian native Tamil language words. The speech parameters like pitch, formants, signal-to-noise ratio, energy, intensity, jitter and shimmer are analyzed. From the results, it has been observed that the acoustic characteristics of deaf speakers differ significantly and their quantitative measure dominates the normal speakers for the words considered. The study also reveals that the informative part of speech in a normal and deaf speakers may be identified using the acoustic features. In addition, these attributes may be used for differential corrections of deaf speaker’s speech signal and facilitate listeners to understand the conveyed information.

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.

BLIND SPEECH SEPARATION FOR ROBOTS WITH INTELLIGENT HUMAN-MACHINE INTERACTION

Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.

Enhanced Frequency-Domain Frost Algorithm Using Conjugate Gradient Techniques for Speech Enhancement

In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless response （MVDR） solution with the correlation matrix inversion, the Frost algorithm implementing the stochastic constrained least mean square （LMS） algorithm can adaptively converge to the MVDR solution in mean-square sense, but with a very slow convergence rate. In this paper, we propose a frequency-domain constrained conjugate gradient （FDCCG） algorithm to speed up the convergence. The devised FDCCG algorithm avoids the matrix inversion and exhibits fast convergence. The speech enhancement experiments for the target speech signal corrupted by two and five interfering speech signals are demonstrated by using a four-channel acoustic-vector-sensor （AVS） micro-phone array and show the superior performance.

High Performance Speech Compression System

Since Pulse Code Modulation emerged in 1937, digitized speech has experienced rapid development due to its outstanding voice quality, reliability, robustness and security in communication. But how to reduce channel width without loss of speech quality remains a crucial problem in speech coding theory. A new full-duplex digital speech communication system based on the Vocoder of AMBE-1000(TM) and microcontroller ATMEL 89C51 is introduced. It shows higher voice quality than current mobile phone system with only a quarter of channel width needed for the latter. The prospective areas in which the system can be applied include satellite communication, IP Phone, virtual meeting and the most important, defence industry.

Artificial Intelligence for Speech Recognition Based on Neural Networks

Speech recognition or speech to text includes capturing and digitizing the sound waves, transformation of basic linguistic units or phonemes, constructing words from phonemes and contextually analyzing the words to ensure the correct spelling of words that sounds the same. Approach: Studying the possibility of designing a software system using one of the techniques of artificial intelligence applications neuron networks where this system is able to distinguish the sound signals and neural networks of irregular users. Fixed weights are trained on those forms first and then the system gives the output match for each of these formats and high speed. The proposed neural network study is based on solutions of speech recognition tasks, detecting signals using angular modulation and detection of modulated techniques.

基于OBE理念的“双线混融”教学模式改革研究——以《语音信号处理》课程为例

本研究以《语音信号处理》课程为例,探讨基于OBE(Outcome-Based Education)理念的“双线混融”教学模式改革。传统的教学模式强调知识的传授,存在知识单一、缺乏实际应用和互动性差等问题;而OBE理念注重学生的能力培养和实践应用能力的提升。本研究提出一种基于OBE理念的“双线混融”的教学模式,在课堂教学与实践环节中相互支持,促进学生的综合能力发展。实践结果表明,采用“双线混融”教学模式后,学生的学习动力和学习效果有明显的提升。学生能够更好地理解和应用语音信号处理相关知识,提高解决问题和实践操作能力。

The laboratory of acoustics,speech and signal processing at the institute of acoustics

The Laboratory of Acoustics,Speech and Signal Processing(LASSP),theunique and superior national key laboratory of ASSP in China,has been foundedat the Inst.of Acoustics,Academia Sinica,Beijing PRC.After three years ofefforts,the construction of the LASSP has been completed successfully and thecertain capability of performing frontier research projects in fundamental theory andapplied technology of sound field and acoustic signal processing has ben formed.A fiexible and complete experimental acoustic signal processing system hasbeen set up in the LASSP.With the remarkable advantage of real time signalprocessing and resource sharing,a wide range of research projects in the field ofASSP can be conducted in the laboratory.The Signal Processing Center of theLASSP is well equipped with many computer research facilities including the

人工智能在音频信号处理中的应用与挑战

人工智能可以通过智能化的算法和模型处理音频信号,从而实现音频的增强、识别及转换等功能。然而,人工智能在音频处理领域的应用也面临一些挑战。首先从自动语音识别、语音合成、音频去噪与增强、情感识别与音频分析4个方面分析人工智能在音频信号处理中的应用,其次从音频信号的复杂性和多变性、数据获取与标注问题、计算资源与效率问题以及隐私与安全问题4个方面分析人工智能在音频信号处理中面临的挑战,最后深入分析应对挑战的对策。

基于机器学习的电力系统语音指令识别算法研究

通过提高电力系统中语音指令识别技术的准确度、实时性和鲁棒性,旨在增强电力系统的可靠性和稳定性。首先分析了电力系统语音信号的预处理方法,包括信号增强、语音帧分割和频谱平滑等技术,在此基础上设计了一种基于高斯混合模型的语音指令识别算法。试验结果表明,该算法在电力系统语音控制场景下具有较高的识别准确率和实时性,同时具备良好的鲁棒性,完成能够满足电力系统复杂环境下的语音指令识别需求。研究还指出了一些改进和完善的方向,以进一步提升算法性能,满足更广泛的实际应用需求。

4th National Conference on Speech,Image,Communication,and Signal Processing,held in Beijing,25—27 October 1989

The 4th National Conference on Speech,Image,Communication and Signal Pro-cessing,which was sponsored by the Institute of Speech,Hearing,and Music Acoustics,Acoustical Society of China and the Institute of Signal Processing,Electronic Society ofChina,was held,25—27 October,1989,at Beijing Institute of Post and Telecommun-ication.The conference drew a registration of 150 from different places in the country,which made it the largest conference in the last eight years.The president of Institute of Speech,Hearing,and Music Acoustics,ASC,professorZHANG Jialu made a openning speech at the openning session,and the honorary presi-dent of Acoustical Society of China,professor MAA Dah-You and the president of

语音信号端点检测方法综述及展望

端点检测是语音信号处理过程中非常重要的一步,它的准确性直接影响语音信号处理的速度和结果,因此端点检测方法的研究,特别是在噪声环境下端点检测的研究,一直是语音信号处理中的热点。从基于时域参数、频域参数、时频参数、模型匹配等方法的角度,较全面地回顾了端点检测方法的发展历程,对各种方法的优缺点进行了比较分析,并给出了这些方法的改进意见,对端点检测未来的研究方向进行了展望。

基于伽马通滤波器组的听觉特征提取算法研究

本文从模拟人类听觉角度出发,给出了基于人耳耳蜗听觉模型的伽马通滤波器组模型,测试语音通过该滤波器组输出得到了高维听觉特征向量.经过主成分分析和离散余弦变换,分别得到了可用于表征说话人的伽马通系数和伽马通滤波器倒谱系数及其衍生特征.实验证明,与传统梅尔倒谱特征相比,采用本文提出特征的说话人识别系统在识别率及鲁棒性上均有明显提高.

一种数字助听器多通道响度补偿方法

该文提出了一种数字助听器非等宽多通道响度补偿方法。该方法研究了完美重构滤波器组分析与综合滤波器的设计方法,并根据人耳对频率的灵敏度特征及对声强的感知特性实现了符合人耳听觉特征的非等宽多通道响度补偿方案。针对典型老年性耳聋患者的实验与仿真结果表明,算法有效补偿了患者缺失的语音高频能量,显著提高了患者的言语辨识率,降低了患者的言语察觉阈。

混沌、分形理论与语音信号处理

本文旨在将新兴的混沌、分形理论引入语音信号处理。本文提出了一种新的语音信号相空间重构方法，分析、统计了语音信号最大Ｌｙａｐｕｎｏｖ指数及分维度的分布，并提出了基于分形码本的语音信号码激励线性预测编码新算法。本文的研究表明，混沌、分形理论在语音信号处理中有良好的应用前景。

语音信号处理实验的改革与实践

随着数字信号处理的发展,语音信号领域越来越受到关注,语音信号处理是实现人机交互和通讯技术的必要方法。语音信号处理教学实验课程在国外著名大学已经得到广泛开设。本文介绍了一门最新开设的将基础知识与理论研究相结合的语音信号处理实验课程。该课程主要由一系列由浅入深的语音信号处理实验构成,通过这些实验,使学生深入理解语音信号领域的前沿知识。在自主实验环节,学生自主提出实验题目及内容,通过小组形式完整地完成一个项目的设计、开发和汇报过程。本文将一个小组的语音分离项目为例,来说明该课程不仅能够培养学生的动手和分析解决问题能力,并能激发学生对相关领域的研究兴趣。

基于任意麦克风阵列的声源二维DOA估计算法研究

对基于麦克风阵列的声源定位技术进行了研究,分析了基于麦克风阵列的远场信号模型,并结合子空间的方法推导出了声源二维(水平角和俯仰角)DOA估计——2D-MUSIC算法,该算法适用于任意拓扑结构的麦克风阵列。利用MATLAB仿真工具,对几种典型阵列结构进行了对比分析,提出了2种新型的三维麦克风阵列:均匀球面阵和三维均匀直线阵。仿真结果表明,提出的DOA估计算法在二维的均匀圆阵、三维的均匀球面阵和三维均匀直线阵中,均能得到较好的DOA估计效果。

基于任意麦克风阵列的近场声源三维定位算法研究

基于麦克风阵列的声源定位技术在通信、语音处理等领域得到广泛的应用。本文从声音传播的基本原理出发,推导出了精确的相对幅度衰减因子,获得了更加准确的信号传播模型。利用该模型并结合子空间算法的思想,提出了适用于任意拓扑结构的麦克风阵列的近场宽带三维声源定位算法。仿真结果表明,该算法在一维均匀直线阵、二维均匀圆阵和三维均匀球面阵中,均能够得到较好的定位效果。

一种基于奇异值分解的带噪语音识别方法

提出了一种抗噪声的语音识别方法 .用于训练和用于测试的语音信号在提取特征之前 ,均需经过相同的奇异值分解滤波 .本文还提出了一种滤波参数的选取方法 .实验证明 ,采用这种方法可以大幅度提高传统隐马尔可夫模型语音识别系统的抗噪声性能 .