• Voice conversion is an newly branch of speech signal processing.

    说话人转换语音信号处理领域中一个前沿的研究分支

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  • There is long time of speech signal processing and it presents the extensive practical field.

    语音信号处理有广泛应用领域,长的研究历史。

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  • CIS algorithm of cochlear implants' speech signal processing is achieved through hardware method.

    文中采用硬件方法人工耳蜗语音信号处理CIS算法加以实现

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  • In this paper, a new linear prediction model in autocorrelation domain for speech signal is presented.

    提出种在自相关语音信号进行线性预测分析的方法。

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  • It presents a new method on reducing additive white noise in speech signal using linear prediction error.

    提出利用线性预测误差去除语音中的加性噪声方法

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  • But it needs prior knowledge of speech signal and its classification and decision making capability is weak.

    存在需要语音信号先验知识分类决策能力弱等缺点

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  • So, in this chapter, the basic principle and algorithm of LPC for speech signal have been discussed in detail.

    所以一章较为详细地讨论语音信号线性预测编码基本原理算法

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  • Describes an algorithm for marking pitch in the location of the highest amplitude of continuous speech signal.

    阐述了一种连续语音信号大幅度的基音标注算法

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  • Speech signal acquisition and processing is the precondition and foundation of human-computer speech interaction.

    语音信号采集处理计算机语音交互前提基础

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  • The speech signal is a analog one, and we can perform its digital transmission by sampling, quantizing and coding.

    话音信号一个模拟信号,通过采样量化编码我们可以实现话音信号的数字传输

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  • This paper proposed a new method on reducing additive white noise in speech signal using linear prediction residual.

    提出利用线性预测残差去除语音中加噪声方法

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  • We utilize GAs to search stimulative code word in CELP when we have had the knowledge of speech signal coding and GAs.

    具备了语音信号编码和遗传算法基本知识后,将遗传算法运用CELP搜索

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  • One of main services in mobile communication is speech and the key technology is the coding and decoding of speech signal.

    语音业务一直移动通信中的主要业务之一,其中关键技术就是语音信号的编码解码

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  • Speech enhancement tries to extract clean speech signal from original one with noise and improve the SNR of speech signal.

    语音增强信号中提取干净语音信号,提高语音信号信噪比。

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  • Speech signal, carrying a large amount of information, is so complex with the character of non-stationary and time-varying.

    携带大量信息语音信号本身非常复杂,并且具有平稳性、时变性等特征

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  • This paper presents a new formulation for recursively computing the reflection (Partial Correlation) coefficients of speech signal.

    本文提出计算反射系数的高效公式——位内推算法。

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  • Based on NLMS, this paper proposes a decorrelation NLMS algorithm (DC-NLMS) by employing auto-correlation of the input speech signal.

    NLMS算法基础,从语音信号相关性的角度出发,提出一种相关NLMS算法(DC -NLMS)。

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  • Speech signal keeps stability in a short time, and this "short - time characteristic" is an important characteristic of speech signal.

    时间里语音信号保持相对稳定这种短时性”语音信号重要特性

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  • The function of the speech processor lies in dealing with speech signal and producing the pulse which electrodes need to stimulate cochlea.

    语音处理器主要作用在于声音采集处理产生电极所需刺激信号

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  • To hide information in speech signal, which is a special kind of audio signals, one must consider the characteristics of human audio system.

    语音信号作为一种特殊信息隐藏载体,其中进行信息隐藏时必须充分考虑人类听觉特性

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  • The proposed method uses the maximum singular value of an cumulant matrix to distinguish between voiced parts of the speech signal and noise.

    提出一种采用累积量矩阵最大奇异实现语音端点检测方法,并引入种自适应的实现方法。

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  • Speech signal is a major channel for human to communicate and to transmit information, making machines understand human language is human's dream.

    语音信号人类交流传递信息主要途径机器听懂人类的语言是人类梦想

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  • People are no longer satisfied with the simple speech signal and transmission of the characters data, but expect use many kinds of expression media.

    人们不再满足单纯语音信号文字数据传输而是期盼使用多种表示媒体。

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  • Speech signal spectrum analysis is the time-varied spectrum analysis. FFT technique on speech signal spectrum analysis are discussed in detail in this paper.

    针对语音信号频谱分析实际上时变频谱分析的特性,详细地讨论了用FFT技术语音进行频谱分析过程中的方法问题。

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  • Speaker Recognition is one of current researching hotspots of speech signal processing, which has many advantaged superiority at identity authentication field.

    说话人识别当前语音信号处理研究热点之一应用身份认证领域具有得天独厚的优势

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  • Since the widely used Hidden Markov model (HMM) in speech recognition is first order Markov model, it can not fully model the temporal dependence of speech signal.

    由于语音识别中被广泛应用马尔可夫模型(HMM)重马尔可夫模型,不能充分地描述语音信号时间相依性。

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  • Based on the statistical experiments, this paper proposes that the feature variables which indicate speech signal are not always strictly obey normal distribution.

    本文统计实验的基础上指出表示语音信号特征变量往往并不严格服从正态分布

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  • This filter can effectively restrain background noise and acquire clear speech signal when applied in speech signal pick-up from the background with complex noise.

    滤波器应用于复杂噪音背景话音信号提取很好地抑制背景噪声从而获得清晰的话音信号。

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  • In this paper, we use full pole model to obtain speech signal LPC, then deduce it's LPCC, and we use the LPCC difference to describe speaker's track dynamic movement.

    本文应用极点模型提取语音信号线性预测系数,推导出其倒谱系数,获得线性预测倒谱差分用以描述说话人声道动态变化。

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  • In this paper, we use full pole model to obtain speech signal LPC, then deduce it's LPCC, and we use the LPCC difference to describe speaker's track dynamic movement.

    本文应用极点模型提取语音信号线性预测系数,推导出其倒谱系数,获得线性预测倒谱差分用以描述说话人声道动态变化。

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