Signal Analysis and Interpretation Laboratory, Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089, USA.
J Acoust Soc Am. 2011 Oct;130(4):EL251-7. doi: 10.1121/1.3634122.
An automatic speech recognition approach is presented which uses articulatory features estimated by a subject-independent acoustic-to-articulatory inversion. The inversion allows estimation of articulatory features from any talker's speech acoustics using only an exemplary subject's articulatory-to-acoustic map. Results are reported on a broad class phonetic classification experiment on speech from English talkers using data from three distinct English talkers as exemplars for inversion. Results indicate that the inclusion of the articulatory information improves classification accuracy but the improvement is more significant when the speaking style of the exemplar and the talker are matched compared to when they are mismatched.
本文提出了一种自动语音识别方法，该方法使用由与说话人无关的声学到发音的反转估计的发音特征。该反转允许仅使用示例主体的发音到声学图从任何说话者的语音声学中估计发音特征。使用来自三个不同英语说话者的示例数据，在英语说话者的语音的广泛类语音分类实验中报告了结果。结果表明，包含发音信息可以提高分类准确性，但当示例和说话者的说话风格匹配时，与不匹配时相比，改进更为显著。
