In mobile speech communication applications, wind noise can lead to a severe reduction of speech quality and intelligibility. Since the performance of speech enhancement algorithms using acoustic microphones tends to substantially degrade in extremely challenging scenarios, auxiliary sensors such as contact microphones can be used. Although contact microphones offer a much lower recorded wind noise level, they come at the cost of speech distortion and additional noise components. Aiming at exploiting the advantages of acoustic and contact microphones for wind noise reduction, in this paper we propose to extend conventional single-microphone dictionary-based speech enhancement approaches by simultaneously modeling the acoustic and contact microphone signals. We propose to train a single speech dictionary and two noise dictionaries and use a relative transfer function to model the relationship between the speech components at the microphones. Simulation results show that the proposed approach yields improvements in both speech quality and intelligibility compared to several baseline approaches, most notably approaches using only the contact microphones or only the acoustic microphone.
翻译:在移动语音通信应用中,风噪声可导致语音质量和智能的大幅下降。由于使用声音麦克风进行语音增强算法在极具挑战性的情况下往往会大幅下降,因此可以使用接触麦克风等辅助传感器。虽然接触麦克风的声响记录要低得多,但其产生的代价却是言语扭曲和增加噪音部件。我们在本文件中提议利用声音和接触麦克风的麦克风的麦克风的好处减少风的噪音。我们提议通过同时模拟声音和接触麦克风的信号来推广传统的单声-麦克风字典语音增强方法。我们提议培训单声词典和两个噪声字典,并使用相对转移功能来模拟麦克风语音组成部分之间的关系。模拟结果表明,与若干基线方法相比,拟议方法在语音质量和智能两方面都会产生改进,最明显的是仅使用接触麦克风或仅使用声麦克风的方法。