论文标题
机器语音识别的盲信还覆盖
Blind Signal Dereverberation for Machine Speech Recognition
论文作者
论文摘要
我们提出了一种方法，以消除通过录音环境的回响引入语音的未知噪声噪声，利用回合环境中的一些培训语音数据以及任何可用的非替代语音数据。使用在长时间的颞窗上计算出的傅立叶变换，理想地覆盖了整个房间的冲动响应，我们将房间引起的卷积转换为对数频谱域中的添加。接下来，我们从收集到的统计数据中计算出光谱域的统计向量，以及对数频谱域中的干净语音。在操作过程中，该归一化向量用于减轻在相同回响条件下记录的复杂语音光谱的回响。这样的复杂的复杂语音光谱用于计算复杂的FDLP光谱图，以用于自动语音识别。
We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal windows, which ideally cover the entire room impulse response, we convert room induced convolution to additions in the log spectral domain. Next, we compute a spectral normalization vector from statistics gathered over reverberated as well as over clean speech in the log spectral domain. During operation, this normalization vectors are used to alleviate reverberations from complex speech spectra recorded under the same reverberant conditions . Such dereverberated complex speech spectra are used to compute complex FDLP-spectrograms for use in automatic speech recognition.
