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Abstract：
Speech is produced by a nonlinear, dynamical Vocal Tract (VT) system, and is transmitted through multiple (air, bone and skin conduction) modes, as captured by the air, bone and throat microphones respectively. Speaker specific characteristics that capture this nonlinearity are rarely used as stand-alone features for speaker modeling, and at best have been used in tandem with well known linear spectral features to produce tangible results. This paper proposes Recurrent Plot (RP) embeddings as stand-alone, non-linear speaker-discriminating features. Two datasets, the continuous multimodal TIMIT speech corpus and the consonant-vowel unimodal syllable dataset, are used in this study for conducting closed-set speaker identification experiments. Experiments with unimodal speaker recognition systems show that RP embeddings capture the nonlinear dynamics of the VT system which are unique to every speaker, in all the modes of speech. The Air (A), Bone (B) and Throat (T) microphone systems, trained purely on RP embeddings perform with an accuracy of 95.81%, 98.18% and 99.74%, respectively. Experiments using the joint feature space of combined RP embeddings for bimodal (A-T, A-B, B-T) and trimodal (A-B-T) systems show that the best trimodal system (99.84% accuracy) performs on par with trimodal systems using spectrogram (99.45%) and MFCC (99.98%). The 98.84% performance of the B-T bimodal system shows the efficacy of a speaker recognition system based entirely on alternate (bone and throat) speech, in the absence of the standard (air) speech. The results underscore the significance of the RP embedding, as a nonlinear feature representation of the dynamical VT system that can act independently for speaker recognition. It is envisaged that speech recognition too will benefit from this nonlinear feature.
摘要：
语音是由非线性产生的，动态声道（VT）系统，并通过多个(空气，骨骼和皮肤传导)模式，被空中捕获，骨和喉咙麦克风分别。捕获这种非线性的说话者特定特征很少用作说话者建模的独立特征，并且充其量与众所周知的线性光谱特征一起使用以产生有形的结果。本文提出了递归图(RP)嵌入作为独立的，非线性说话人辨别特征。两个数据集,连续多模态TIMIT语音语料库和辅音元音单峰音节数据集，在这项研究中用于进行闭集说话者识别实验。单峰说话人识别系统的实验表明，RP嵌入捕获了每个说话人独有的VT系统的非线性动力学，在所有的语音模式中。空气（A）骨骼（B）和喉咙（T）麦克风系统，纯粹在RP嵌入上训练，准确率为95.81%，98.18%和99.74%,分别。使用双峰组合RP嵌入的联合特征空间的实验(A-T，A-B,B-T）和三峰（A-B-T）系统表明，最佳的三峰系统（99.84％的精度）与使用频谱图（99.45％）和MFCC（99.98％）的三峰系统相当。B-T双模系统的98.84％性能显示了完全基于替代（骨骼和喉咙）语音的说话人识别系统的功效，在没有标准(空中)演讲的情况下。结果强调了RP嵌入的重要性，作为动态VT系统的非线性特征表示，可以独立地进行说话人识别。可以设想,语音识别也将受益于该非线性特征。
