英语翻译Abstract—An effective voice activity detection (VAD) algorithmis proposed for improving speech recognition performance innoisy environments.The approach is based on the determinationof the speech/nonspeech divergence by means of special

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英语翻译Abstract—An effective voice activity detection (VAD) algorithmis proposed for improving speech recognition performance innoisy environments.The approach is based on the determinationof the speech/nonspeech divergence by means of special

英语翻译Abstract—An effective voice activity detection (VAD) algorithmis proposed for improving speech recognition performance innoisy environments.The approach is based on the determinationof the speech/nonspeech divergence by means of special
英语翻译
Abstract—An effective voice activity detection (VAD) algorithm
is proposed for improving speech recognition performance in
noisy environments.The approach is based on the determination
of the speech/nonspeech divergence by means of specialized order
statistics filters (OSFs) working on the subband log-energies.
This algorithm differs from many others in the way the decision
rule is formulated.Instead of making the decision based on the
current frame,it uses OSFs on the subband log-energies which
significantly reduces the error probability when discriminating
speech from nonspeech in a noisy signal.Clear improvements
in speech/nonspeech discrimination accuracy demonstrate the
effectiveness of the proposed VAD.It is shown that an increase of
the OSF order leads to a better separation of the speech and noise
distributions,thus allowing a more effective discrimination and
a tradeoff between complexity and performance.The algorithm
also incorporates a noise reduction block working in tandem with
the VAD and showed to further improve its accuracy.A previous
noise reduction block also improves the accuracy in detecting
speech and nonspeech.The experimental analysis carried out on
the AURORA databases and tasks provides an extensive performance
evaluation together with an exhaustive comparison to the
standard VADs such as ITU G.729,GSM AMR,and ETSI AFE for
distributed speech recognition (DSR),and other recently reported
VADs.
Index Terms—Noise reduction,robust speech recognition,
speech/nonspeech detection,subband order statistics filters.

英语翻译Abstract—An effective voice activity detection (VAD) algorithmis proposed for improving speech recognition performance innoisy environments.The approach is based on the determinationof the speech/nonspeech divergence by means of special
抽象的一个有效的语音活动检测（VAD）算法
提出了提高语音识别性能
嘈杂的环境.该方法是基于对测定
由专门的命令意味着语音/ nonspeech分歧
统计滤波器（性质,OSFS）关于子带日志精力工作.
该算法不同于许多其他的方式的决定
规则的制订.而不是作出决定的基础上
当前帧,它使用的频带数的能量性质,OSFS
大大减少了错误的概率时歧视
从nonspeech讲话在嘈杂的信号.明显改善
在语音/ nonspeech歧视准确性展示
建议VAD方案的有效性.结果表明,增加的一
在OSF秩序导致了更好的语音和噪声分离
分布,从而使一个更加有效的歧视和
复杂性和性能之间的权衡.该算法
还集成了降噪块串联与
VAD与表明的,以进一步提高其准确性.阿前
降噪区块还提高了检测的准确性
言论和nonspeech.实验进行了分析
震旦的数据库和任务提供了广泛的性能
连同一份详尽的评估相比,
例如国际电联G.729的,GSM的AMR的,外汇局和ETSI标准威斯为
分布式语音识别（DSR路由）,和其他最近报告
威斯.
指数计算,噪声降低,稳健语音识别,
语音/ nonspeech检测,子带顺序统计滤波器.

摘要——提出一种有效的语音活动检测(VAD)算法来改进噪声环境中的语音识别性能。该方法基于通过作用于子带对数能量上的专门的顺序统计过滤算法(OSFs)来检测语音/非语音的散度。该算法不同于决策规则所规定的许多其他的算法。不同于基于当前帧来作决策，它在子带对数能量上使用OSFs，这使得它在噪声信号中从非语音中识别语音时，明显减少了误差概率。语音/非语音鉴别准确度的明显改进说明了所提出的VAD的有效性...

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摘要——提出一种有效的语音活动检测(VAD)算法来改进噪声环境中的语音识别性能。该方法基于通过作用于子带对数能量上的专门的顺序统计过滤算法(OSFs)来检测语音/非语音的散度。该算法不同于决策规则所规定的许多其他的算法。不同于基于当前帧来作决策，它在子带对数能量上使用OSFs，这使得它在噪声信号中从非语音中识别语音时，明显减少了误差概率。语音/非语音鉴别准确度的明显改进说明了所提出的VAD的有效性。这表明OSF顺序的增加导致了语音和噪声分布的较好的分离，因此使它在复杂性和性能之间得到更有效的鉴别和权衡。该算法还包含了噪声降低块与VAD相配合，并且在正确性上有了更进一步的改善。之前的噪声降低块也改进了检测语音和非语音的正确性。实验分析是在AURORA数据库上实现的，并且与像ITU G.729, GSM AMR和用于分布式语音识别(DSR)的ETSI AFE那样的标准VADs和其他最近报道的VADs进行了全面的比较，同时提供了大量的性能评估。
索引词——噪声降低，鲁棒语音识别，语音/非语音检测，子带顺序统计过滤。

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