Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional meth...Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.展开更多
Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obs...Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical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.展开更多
Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discusse...Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper. The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed program and Basis Pursuit (BP). The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals. It is proved by the simulation results of the mixed program which is an improved version of the mixed program. The second one is the well known large DCT coefficients of voiced speech focus on low frequency. In line with this feature, a special Gaussian and Partial Identity Joint (GPIJ) matrix is constructed as the sensing matrix for voiced speech signals. Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.展开更多
In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-wri...In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. In this study, a signal detection method to remove the noise for actual speech signals is proposed by using Bayesian estimation with the aid of bone-conducted speech. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal is theoretically derived. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. The effectiveness of the proposed method is experimentally confirmed by applying it to air- and bone-conducted speeches measured in real environment under the existence of surrounding background noise.展开更多
Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed s...Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.展开更多
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 s...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.展开更多
In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine ...In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.展开更多
This paper presents a novel non-contact heart rate extraction method from vowel speech signals. The proposed method is based on modeling the relationship between speech production of vowel speech signals and heart act...This paper presents a novel non-contact heart rate extraction method from vowel speech signals. The proposed method is based on modeling the relationship between speech production of vowel speech signals and heart activities for humans where it is observed that the moment of heart beat causes a short increment （evolution） of vowel speech formants. The short-time Fourier transform （STFT） is used to detect the formant maximum peaks so as to accurately estimate the heart rate. Compared with traditional contact pulse oximeter, the average accuracy of the proposed non-contact heart rate extraction method exceeds 95%. The proposed non-contact heart rate extraction method is expected to play an important role in modern medical applications.展开更多
The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech ...The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech recognition. Combining the traditional direction finding technique of MUltiple SIgnal Classification (MUSIC) with the focusing matrix method, we improve the resolving power of the microphone array for multiple speech sources.As one application of finding Direction of Arrival (DOA), a new microphone-array system for noise reduction is proposed. The new system is based on maximum likelihood estimate technique which reconstruct superimposed signals from different directions by using DOA information. The DOA information is got in terms of focusing MUSIC method which has been proven to have high performance than conventional MUSIC method on speaker localization[1].展开更多
To address the challenges of non-cooperative and remote acoustic detection, an all-fiber laser Doppler vibrometer(LDV) is established. The all-fiber LDV system can offer the advantages of smaller size, lightweight des...To address the challenges of non-cooperative and remote acoustic detection, an all-fiber laser Doppler vibrometer(LDV) is established. The all-fiber LDV system can offer the advantages of smaller size, lightweight design and robust structure, hence it is a better fit for remote speech detection. In order to improve the performance and the efficiency of LDV for long-range hearing, the speech enhancement technology based on optimally modified log-spectral amplitude(OM-LSA) algorithm is used. The experimental results show that the comprehensible speech signals within the range of 150 m can be obtained by the proposed LDV. The signal-to-noise ratio(SNR) and mean opinion score(MOS) of the LDV speech signal can be increased by 100% and 27%, respectively, by using the speech enhancement technology. This all-fiber LDV, which combines the speech enhancement technology, can meet the practical demand in engineering.展开更多
目的采用真耳分析和不同信噪比下的言语识别率测试初步评价双耳信号对传式(BICROS)助听器的助听效果。方法选自2019年2月~2020年1月20例一侧全聋、一侧听力为中度听力损失(简称听损)患者在通过真耳分析验证达到验配公式目标值后,分别在...目的采用真耳分析和不同信噪比下的言语识别率测试初步评价双耳信号对传式(BICROS)助听器的助听效果。方法选自2019年2月~2020年1月20例一侧全聋、一侧听力为中度听力损失(简称听损)患者在通过真耳分析验证达到验配公式目标值后,分别在单耳佩戴助听器和双耳佩戴BICROS助听器干预前后的安静环境和噪声环境进行言语测试和声场助听听阈评估。结果患者单耳佩戴助听器与双耳佩戴BICROS助听器在0.5~4 kHz的声场助听听阈高度相关(P值均<0.001);声场下65 dB SPL言语识别率测试显示:安静环境、噪声环境(信噪比为5 dB)下双耳佩戴BICROS助听器与仅较好耳佩戴助听器相比差异均有统计学意义(P值均<0.001)。安静环境下裸耳和双耳佩戴BICROS的最大言语识别率显著相关(r=0.911,P<0.001)。结论安静环境、噪音环境下单耳极重度非对称性听损患者通过较好耳单侧佩戴助听器和使用BICROS助听器均能受益,双耳佩戴BICROS助听器在噪声环境下受益更明显。展开更多
传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音...传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音信号翻译机器人的语义纠错模型,采用随时间反向传播(Backpropagation Through Time,BPTT)循环训练核验方式,以确保纠错的准确性。测试结果显示,经过3个阶段测试,选定的5段语音材料的纠错识别率成功控制在10%以下,表明基于语音信号的跨语种交互翻译机器人语义纠错方法高效,具有实际应用价值。展开更多
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...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.展开更多
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...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.展开更多
文摘Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.
文摘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.
基金Supported by the National Natural Science Foundation of China (No. 60971129)the National Research Program of China (973 Program) (No. 2011CB302303)the Scientific Innovation Research Program of College Graduate in Jiangsu Province (No. CXLX11_0408)
文摘Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper. The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed program and Basis Pursuit (BP). The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals. It is proved by the simulation results of the mixed program which is an improved version of the mixed program. The second one is the well known large DCT coefficients of voiced speech focus on low frequency. In line with this feature, a special Gaussian and Partial Identity Joint (GPIJ) matrix is constructed as the sensing matrix for voiced speech signals. Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.
文摘In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. In this study, a signal detection method to remove the noise for actual speech signals is proposed by using Bayesian estimation with the aid of bone-conducted speech. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal is theoretically derived. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. The effectiveness of the proposed method is experimentally confirmed by applying it to air- and bone-conducted speeches measured in real environment under the existence of surrounding background noise.
基金Supported by the National Natural Science Foundation of China (No. 60971129)the National Research Program of China (973 Program) (No. 2011CB302303)the Scientific Innovation Research Program of College Graduate in Jiangsu Province (No. CXLX11_0408)
文摘Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.
文摘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.
基金Supported by the Naltural Science Foundation of Hunan Province(97JJY1006)Open Foundation of Stalte Key Lab. of Theory and Chief Technology on ISN of Xidian University(991894102)
文摘In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.
文摘This paper presents a novel non-contact heart rate extraction method from vowel speech signals. The proposed method is based on modeling the relationship between speech production of vowel speech signals and heart activities for humans where it is observed that the moment of heart beat causes a short increment （evolution） of vowel speech formants. The short-time Fourier transform （STFT） is used to detect the formant maximum peaks so as to accurately estimate the heart rate. Compared with traditional contact pulse oximeter, the average accuracy of the proposed non-contact heart rate extraction method exceeds 95%. The proposed non-contact heart rate extraction method is expected to play an important role in modern medical applications.
文摘The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech recognition. Combining the traditional direction finding technique of MUltiple SIgnal Classification (MUSIC) with the focusing matrix method, we improve the resolving power of the microphone array for multiple speech sources.As one application of finding Direction of Arrival (DOA), a new microphone-array system for noise reduction is proposed. The new system is based on maximum likelihood estimate technique which reconstruct superimposed signals from different directions by using DOA information. The DOA information is got in terms of focusing MUSIC method which has been proven to have high performance than conventional MUSIC method on speaker localization[1].
基金supported by the National Natural Science Foundation of China(No.61205143)
文摘To address the challenges of non-cooperative and remote acoustic detection, an all-fiber laser Doppler vibrometer(LDV) is established. The all-fiber LDV system can offer the advantages of smaller size, lightweight design and robust structure, hence it is a better fit for remote speech detection. In order to improve the performance and the efficiency of LDV for long-range hearing, the speech enhancement technology based on optimally modified log-spectral amplitude(OM-LSA) algorithm is used. The experimental results show that the comprehensible speech signals within the range of 150 m can be obtained by the proposed LDV. The signal-to-noise ratio(SNR) and mean opinion score(MOS) of the LDV speech signal can be increased by 100% and 27%, respectively, by using the speech enhancement technology. This all-fiber LDV, which combines the speech enhancement technology, can meet the practical demand in engineering.
文摘目的采用真耳分析和不同信噪比下的言语识别率测试初步评价双耳信号对传式(BICROS)助听器的助听效果。方法选自2019年2月~2020年1月20例一侧全聋、一侧听力为中度听力损失(简称听损)患者在通过真耳分析验证达到验配公式目标值后,分别在单耳佩戴助听器和双耳佩戴BICROS助听器干预前后的安静环境和噪声环境进行言语测试和声场助听听阈评估。结果患者单耳佩戴助听器与双耳佩戴BICROS助听器在0.5~4 kHz的声场助听听阈高度相关(P值均<0.001);声场下65 dB SPL言语识别率测试显示:安静环境、噪声环境(信噪比为5 dB)下双耳佩戴BICROS助听器与仅较好耳佩戴助听器相比差异均有统计学意义(P值均<0.001)。安静环境下裸耳和双耳佩戴BICROS的最大言语识别率显著相关(r=0.911,P<0.001)。结论安静环境、噪音环境下单耳极重度非对称性听损患者通过较好耳单侧佩戴助听器和使用BICROS助听器均能受益,双耳佩戴BICROS助听器在噪声环境下受益更明显。
文摘传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音信号翻译机器人的语义纠错模型,采用随时间反向传播(Backpropagation Through Time,BPTT)循环训练核验方式,以确保纠错的准确性。测试结果显示,经过3个阶段测试,选定的5段语音材料的纠错识别率成功控制在10%以下,表明基于语音信号的跨语种交互翻译机器人语义纠错方法高效,具有实际应用价值。
文摘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.
基金The work is supported by Regional Innovation Cooperation Project of Sichuan Province(Grant No.22QYCX0082)Jian-Guo Wei received the grant,and the Science and Technology Plan of Qinghai Province,China(Grant No.2019-ZJ-7012)Xiu Juan Ma received the grant.
文摘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.
