###### Abstract  
The development of distant-talk measurement systems has been attracting attention since they can be applied to many situations such as security and disaster relief. One such system that uses a device called a laser Doppler vibrometer (LDV) to acquire sound by measuring an object's vibration caused by the sound source has been proposed. Different from traditional microphones, an LDV can pick up the target sound from a distance even in a noisy environment. However, the acquired sounds are greatly distorted due to the object's shape and frequency response. Due to the particularity of the degradation of observed speech, conventional methods cannot be effectively applied to LDVs. We propose two speech enhancement methods that are based on two-stage processing with deep neural networks for LDVs. With the first proposed method, the amplitude spectrum of the observed speech is first restored. The phase difference between the observed and clean speech is then estimated using the restored amplitude spectrum. With the other proposed method, the low-frequency components of the observed speech are first restored. The high-frequency components are then estimated by the restored low-frequency components. The evaluation results indicate that they improved the observed speech in sound quality, deterioration degree, and intelligibility. distant-talking speech measurement; speech enhancement; deep neural network; laser Doppler vibrometer +
Footnote †: journal: _Applied Sciences_  
phase delay occurs due to the reflective and vibration characteristics of the irradiated object. Moreover, there is a lack of observed speech components in the small amplitude response frequency bands. Therefore, to obtain high-quality speech using an LDV, it is necessary to apply processing such as non-stationary noise suppression and high-frequency component restoration to the observed speech. Speech enhancement methods for LDVs have been proposed [9; 10; 11; 12]. [PERSON] et al. [9] developed a method of first using a band-pass filter to filter out the noise in the non-voice frequency band of the acquired speech and then using a Wiener filter to reduce the noise of the speech signal. [PERSON] et al. [10] proposed acquiring speech with two LDVs then applying the coherent-to-diffuse ratio (CDR) and multi-channel linear prediction (MCLP) between the acquired two-channel signals for noise reduction. However, these methods only execute noise-reduction processing for the acquired speech, and any components missing due to the frequency response are not restored. [PERSON] et al. [11] proposed a method for encoding the mapping between observed speech by using an LDV and clean speech as an auxiliary feature to improve the speech recognition accuracy in a noisy environment, but this method still does not solve the problem of degradation caused by the vibration characteristics of the irradiated object. In recent years, deep neural networks (DNNs) have shown excellent results in speech signal processing [13; 14; 15; 16; 17]. With these conventional speech enhancement methods, the mapping between the input power spectrum and target power spectrum can be learned through the network, and the phase of the observed speech is generally used when restoring the speech [13; 14] or directly inputting the waveform into the network to learn the mapping of the observed speech and clean speech [15]. The research on phase is also gradually attracting attention. When the phase information is unknown, the initial phase spectrum can be learned from the power spectrum and then adjusted using the Griffin-Lim algorithm (GLA) [16; 17]. Since the observed speech has various types of deterioration, conventional speech enhancement methods cannot be effectively applied directly to the observed speech from LDVs. Considering the deterioration of observed speech, we propose two speech-enhancement methods that are based on the frequency and time domains, respectively. Both methods use multiple DNNs to handle different types of deterioration.