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The use of Voice Source Features for Sung Speech Recognition

机译：使用语音源特征来唱歌语音识别

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In this paper, we ask whether vocal source features (pitch, shimmer, jitter, etc) can improve the performance of automatic sung speech recognition, arguing that conclusions previously drawn from spoken speech studies may not be valid in the sung speech domain. We first use a parallel singing/speaking corpus (NUS-48E) to illustrate differences in sung vs spoken voicing characteristics including pitch range, syllables duration, vibrato, jitter and shimmer. We then use this analysis to inform speech recognition experiments on the sung speech DSing corpus, using a state of the art acoustic model and augmenting conventional features with various voice source parameters. Experiments are run with three standard (increasingly large) training sets, DSing1 (15.1 hours), DSing3 (44.7 hours) and DS-ing30 (149.1 hours). Pitch combined with degree of voicing produces a significant decrease in WER from 38.1% to 36.7% when training with DSing1 however smaller decreases in WER observed when training with the larger more varied DSing3 and DSing30 sets were not seen to be statistically significant. Voicing quality characteristics did not improve recognition performance although analysis suggests that they do contribute to an improved discrimination between voiced/unvoiced phoneme pairs.
机译：在本文中，我们询问声乐源特征（音高，闪光，抖动等）是否可以提高自动演讲识别的性能，争论以前从口语讲话中汲取的结论可能在Sung语音域中可能无效。我们首先使用并行唱歌/讲词组（NUS-48E）来说明SUNG VS口语特性的差异，包括音调范围，音节持续时间，颤音，抖动和闪光。然后，我们使用该分析来向语音识别实验告知Sung语音DSing语料库，使用本领域的声学模型和具有各种语音源参数的传统特征。实验用三个标准（越来越大的）训练套，Dsing1（15.1小时），Dsing3（44.7小时）和DS-Ing30（149.1小时）。当用Dsing1的训练训练时，随着DSing1的训练，在训练中培训越来越多的DSing3和DSing30套件时，螺距与发声的程度会产生显着的38.1％至36.7％的显着降低。虽然分析表明他们确实有助于改善声音/清晰的音素对之间的改善歧视，但发声质量特征没有提高识别性能。

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