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    • 81. 发明申请
    • A METHOD AND PORTABLE APPARATUS FOR PERFORMING SPOKEN LANGUAGE TRANSLATION
    • 一种用于执行语音翻译的方法和便携式设备
    • WO00045374A1
    • 2000-08-03
    • PCT/US1999/027874
    • 1999-11-24
    • G06F17/27G06F17/28G10L15/06G10L15/20
    • G06F17/2765G06F17/271G06F17/2755G06F17/2827G06F17/2872G10L15/07G10L15/20
    • In a portable unit, a method for performing spoken language translation. The method includes the steps of receiving (302) at least one speech input comprising at least one source language, and recognizing (306) at least one source expression of the at least one source language. The method also includes the steps of translating (308) the recognized at least one source expression from the at least one source language to at least one target language, synthesizing (312) at least one speech output from the translated at least one target language, and providing (314) the at least one speech output. A set of source language expressions and a set of target language expressions are remotely extensible in some embodiments, using various communication methods. One embodiment further comprises the step of minimizing (1702) misrecognitions of the at least one source expression, wherein the misrecognitions result from factors comprising noise and speaker variation.
    • 在便携式单元中,执行口语翻译的方法。 该方法包括以下步骤:接收(302)包括至少一种源语言的至少一个语音输入,以及识别(306)所述至少一种源语言的至少一个源表达式。 该方法还包括将识别的至少一个源表达从至少一个源语言翻译(308)到至少一个目标语言的步骤,从翻译的至少一个目标语言合成(312)至少一个语音输出, 以及提供(314)所述至少一个语音输出。 使用各种通信方法,在一些实施例中,源语言表达式和一组目标语言表达式可远程扩展。 一个实施例还包括最小化(1702)至少一个源表达式的错误识别的步骤,其中由包括噪声和说话者变化的因素导致误识别。
    • 82. 发明申请
    • IMPROVED NOISE SPECTRUM TRACKING FOR SPEECH ENHANCEMENT
    • 改进的噪声跟踪用于语音增强
    • WO00036592A1
    • 2000-06-22
    • PCT/US1999/029901
    • 1999-12-16
    • G10L15/20G10L21/02
    • G10L21/0208G10L15/20G10L21/0216G10L2021/02168
    • A spectrum-based speech enhancement system estimates and tracks the noise spectrum of a mixed speech and noise signal. The system frames and windows a digitized signal and applies the frames to a fast Fourier transform processor to generate discrete Fourier transformed (DFT) signals representing the speech plus noise signal. The system calculates the power spectrum of each frame. The speech enhancement system employs a leaky integrator that is responsive to identified noise-only components of the signal. The leaky integrator has an adaptive time-constant which compensates for non-stationary environmental noise. In addition, the speech enhancement system identified noise-only intervals by using a technique that monitors the Teager energy of the signal. The transition between noise-only signals and speech plus noise signals is softened by being made non-binary. Once the noise spectrum has been estimated, it is used to generate gain factors that multiply the DFT signals to produce noise-reduced DFT signals. The gain factors are generated based on an audible noise threshold. The method generates audible a priori and a posteriori signal to noise ratio signals and then calculates audible gain signals from these values.
    • 基于频谱的语音增强系统估计和跟踪混合语音和噪声信号的噪声谱。 系统帧和窗口是数字化信号,并将帧应用于快速傅里叶变换处理器,以产生表示语音加噪声信号的离散付里叶变换(DFT)信号。 系统计算每帧的功率谱。 语音增强系统采用泄漏积分器,其响应于所识别的仅噪声信号分量。 泄漏积分器具有补偿非平稳环境噪声的自适应时间常数。 此外,语音增强系统通过使用监视信号的Teager能量的技术来识别仅噪声间隔。 仅噪声信号和语音加噪声信号之间的转换通过非二进制软化。 一旦已经估计出噪声频谱,它被用于产生乘以DFT信号以产生噪声降低的DFT信号的增益因子。 基于可听噪声阈值产生增益因子。 该方法产生可听的先验和后验信噪比信号,然后从这些值计算可听增益信号。
    • 83. 发明申请
    • SPEECH RECOGNITION METHOD IN A NOISY ACOUSTIC SIGNAL AND IMPLEMENTING SYSTEM
    • 语音信号和实现系统中的语音识别方法
    • WO00031728A1
    • 2000-06-02
    • PCT/FR1999/002852
    • 1999-11-19
    • G10L15/20
    • G10L15/20
    • The invention concerns a method and a system for speech recognition in a noisy acoustic signal. In a preferred embodiment, the system (2) comprises modules (30) for detecting speech and for producing a noise model (31), a module (40) quantifying the noise energy level and comparing with predetermined energy level ranges, a parametering chain (5) comprising an optional module for noise correction (51), with Wiener filter, a module (52) for calculating spectral energy in Bark windows, a module (50, 530) for applying a configuration of shifted values (531), by adding said values to Bark coefficients, based on the quantification (40) to modify the parameterization, a module (54) for calculating parameter vectors, and a unit (6) for pattern recognition, performing speech recognition by comparison with pre-recorded parameter vectors during a learning phase.
    • 本发明涉及一种用于噪声声信号中的语音识别的方法和系统。 在优选实施例中,系统(2)包括用于检测语音和用于产生噪声模型(31)的模块(30),对噪声能级进行量化并与预定能级范围比较的模块(40),参数链( 5)包括用于噪声校正的可选模块(51),具有维纳滤波器,用于计算Bark窗口中的频谱能量的模块(52),用于通过添加移位值(531)来构造的模块(50,530) 基于用于修改参数化的量化(40),用于计算参数矢量的模块(54)和用于模式识别的单元(6),将所述值表示为Bark系数,通过与预先记录的参数向量进行比较来执行语音识别 学习阶段
    • 85. 发明申请
    • METHOD FOR SUPPRESSING NOISE IN A DIGITAL SPEECH SIGNAL
    • 用于在数字语音信号中抑制噪声的方法
    • WO99014739A1
    • 1999-03-25
    • PCT/FR1998/001981
    • 1998-09-16
    • G10L15/20G10L15/02G10L21/02G10L21/0208G10L21/0232G10L21/0264G10L25/90H04B3/20G10L3/02
    • G10L21/0208G10L21/0232G10L21/0264G10L21/0364G10L25/90
    • The invention concerns a method for suppressing noise in a digital speech signal processed by successive frames which consists in: computing the signal spectral components (Sn,f, Sn,i) on each frame; computing the maximised estimations (B'?n,i?) of spectral components of the noise included in the speech signal; carrying out a harmonic analysis of the signal to estimate a pitch; carrying out a spectral subtraction comprising at least a step consisting in subtracting respectively, from each spectral component of the speech signal on the frame (Sn,f), a quantity depending on parameters including the maximised estimation of the noise corresponding spectral component and the estimated pitch; and applying to the subtraction result a transform towards the time domain to construct an enhanced speech signal (s ).
    • 本发明涉及一种用于抑制由连续帧处理的数字语音信号中的噪声的方法,其中包括:计算每帧上的信号频谱分量(Sn,f,Sn,i) 计算包括在语音信号中的噪声的频谱分量的最大化估计(B'?n,i?); 对信号进行谐波分析以估计音高; 执行频谱减法,其包括至少包括从帧(Sn,f)上的语音信号的每个频谱分量中减去取决于包括噪声对应频谱分量的最大化估计的参数的量的步骤,以及估计 沥青; 并且向减法结果应用向时域的变换以构建增强的语音信号(s 3)。