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    • 1. 发明公开
    • METHOD AND SYSTEM FOR GENERATING ADVANCED FEATURE DISCRIMINATION VECTORS FOR USE IN SPEECH RECOGNITION
    • VERFAHREN UND SYSTEM ZUR ERZEUGUNG ERWEITERTER MERKMALSUNTERSCHEIDUNGSVEKTOREN ZUR VERWENDUNG IN EERER SPRACHERKENNUNG
    • EP3042377A4
    • 2017-08-30
    • EP14763371
    • 2014-03-17
    • SHORT KEVIN MHONE BRIAN
    • SHORT KEVIN MHONE BRIAN
    • G10L17/02G10L15/02
    • G10L15/02G10L25/03G10L25/18G10L25/21G10L25/24G10L25/93G10L2015/025
    • A method of renormalizing high-resolution oscillator peaks, extracted from windowed samples of an audio signal, is disclosed. Feature vectors are generated for which variations in both fundamental frequency and time duration of speech are substantially mitigated. The feature vectors may be aligned within a common coordinate space, free of those variations in frequency and time duration that occurs between speakers, and even over speech by a single speaker, to facilitate a simple and accurate determination of matches between those AFDVs generated from a sample of the audio signal and corpus AFDVs generated for known speech at the phoneme and sub-phoneme level. The renormalized feature vectors can be combined with traditional feature vectors such as MFCCs, or they can be used exclusively to identify voiced, semi-voiced and unvoiced sounds.
    • 公开了从音频信号的加窗样本中提取的重新归一化高分辨率振荡器峰值的方法。 生成特征矢量,其基本上减轻了语音的基本频率和持续时间的变化。 特征向量可以在公共坐标空间内对齐,不存在扬声器之间发生的频率和持续时间中的这些变化,并且甚至在单个扬声器的语音之上,以便于简单和精确地确定从一个扬声器生成的那些AFDV之间的匹配 在音素和子音素层次为已知语音生成的音频信号和语料库AFDV的样本。 重新归一化的特征向量可以与诸如MFCC的传统特征向量组合,或者它们可以专门用于识别有声,半有声和无声声音。