Noise Reduction Combining a General Microphone and a Throat Microphone.

In this study, we propose a method to reduce noise from speech obtained from a general microphone using the information of a throat microphone. A throat microphone records a sound by detecting the vibration of the skin surface near the throat directly. Therefore, throat microphones are less prone to noise than ordinary microphones. However, as the acoustic characteristics of the throat microphone differ from those of ordinary microphones, its sound quality degrades. To solve this problem, this study aims to improve the speech quality while suppressing the noise of a general microphone by using the information recorded by a throat microphone as reference information to extract the speech signal in general microphones. In this paper, the framework of the proposed method is formulated, and several experiments are conducted to evaluate the noise suppression and speech quality improvement effects of the proposed method.

Recurrence plot embeddings as short segment nonlinear features for multimodal speaker identification using air, bone and throat microphones.

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.