Speech enhancement has been widely investigated for several decades, but by modifying only the amplitude spectrum of a speech signal, ignoring the phase spectrum, which has been regarded as an unimportant feature. However, it was recently reported that the phase spectrum plays an important role in speech quality and intelligibility. In this paper, we propose a phase reconstruction method based on harmonic enhancement using the fundamental frequency and phase distortion feature. This feature is known to show fluctuations in the phase spectrum with respect to time and frequency. We estimate the speech phase spectrum by considering the relationship between harmonic phase spectra. Experimental evaluations indicate that the proposed phase reconstruction method improves speech quality in various noisy environments.

Speech enhancement in noisy environments has been widely investigated by modifying only the amplitude spectrum of the speech signal, while the phase spectrum, which is regarded as an unimportant feature, is ignored. However, it has recently been reported that the phase spectrum plays an important role in the intelligibility and quality of speech. We propose a speech-enhancement method with phase reconstruction, which estimates inartificial phase spectrum by using the time-frequency feature called phase distortion, though a conventional phase reconstruction estimates artificial one. The objective experimental results indicate improvement in speech quality with and efficiency of the proposed method.

Desired/undesired speech discrimination is as important as speech/non-speech discrimination to achieve useful applications such as speech interfaces and teleconferencing systems. Conventional methods of voice activity detection (VAD) utilize the directional information of sound sources to distinguish desired from undesired speech. However, these methods have to utilize multiple microphones to estimate the directions of sound sources. Here, we propose a new method to discriminate desired from undesired speech with a single microphone. We assumed that the desired talkers would be close to the microphone, and the proposed method could distinguish close/distant-talking speech from observed signals based on the kurtosis of the linear prediction (LP) residual signals. The experimental results revealed that the proposed method could distinguish close-talking speech from distant-talking speech within a 10% equal error rate (EER) in ordinary reverberant environments with less processing time.

Recently, parametric loudspeakers have been utilized to reproduce an audible sound in a particular area by an ultrasound. However, parametric loudspeakers have lower sound quality than general electrodynamic loudspeakers because it is difficult to reproduce an audible sound at lower frequency. Moreover, the reproduced sound includes harmonic distortions causing deterioration of sound quality. Thus, in this paper, we attempt to improve sound quality of parametric loudspeakers. It depends on a modulation technique for designing the amplitude modulated wave. Therefore, we propose a new modulation method to reproduce audible sound with high quality. The proposed method controls a weight of double sidebands for reducing harmonic distortions at higher frequency and emphasizing power at lower frequency. We carried out evaluation experiments to confirm the effectiveness of the proposed method. The results confirmed that the proposed method could improve sound quality of parametric loudspeakers.

In a number of engineering fields, information on the distance to the target is very important. We previously proposed an acoustic distance measurement method based on interference between the transmitted and reflected waves, which can be used for distance measurement over a short range. In the previously proposed method, a sound source of the transmitted wave must be known in advance. In the present paper, we propose a new acoustic distance measurement method based on phase interference obtained using the cross-spectral method with adjacent microphones, which does not require the condition that a sound source of the transmitted wave is known. Finally, we confirmed the validity and effectiveness of the newly proposed method through both computer simulation and evaluation experiment in a real environment.

In this paper, we newly introduce a collection of databases and evaluation tools called CENSREC-4, which is an evaluation framework for distant-talking speech under hands-free conditions. Distant-talking speech recognition is crucial for a handsfree speech interface. Therefore, we measured room impulse responses to investigate reverberant speech recognition in various environments. The data contained in CENSREC-4 are connected digit utterances, as in CENSREC-1. Two subsets are included in the data: basic data sets and extra data sets. The basic data sets are used for the evaluation environment for the room impulse response-convolved speech data. The extra data sets consist of simulated and recorded data. An evaluation framework is only provided for the basic data sets as evaluation tools. The results of evaluation experiments proved that CENSREC-4 is an effective database for evaluating the new dereverberation method because the traditional dereverberation process had difficulty sufficiently improving the recognition performance. Index Terms: Various environments, Impulse response, Convolution, Real recorded data, Evaluation framework

Voice activity detection (VAD) plays an important role in speech processing including speech recognition, speech enhancement, and speech coding in noisy environments. We developed an evaluation framework for VAD in such environments, called corpus and environment for noisy speech recognition 1 concatenated (CENSREC-1-C). This framework consists of noisy continuous digit utterances and evaluation tools for VAD results. By adoptiong two evaluation measures, one for frame-level detection performance and the other for utterance-level detection performance, we provide the evaluation results of a power-based VAD method as a baseline. When using VAD in speech recognizer, the detected speech segments are extended to avoid the loss of speech frames and the pause segments are then absorbed by a pause model. We investigate the balance of an explicit segmentation by VAD and an implicit segmentation by a pause model using an experimental simulation of segment extension and show that a small extension improves speech recognition.

Distant-talking speech recognition in noisy environments is indispensable for self-moving robots or teleconference systems. However, background noise and room reverberations seriously degrade the sound-capture quality in real acoustic environments. A microphone array is an ideal candidate as an effective method for capturing distant-talking speech. AMNOR (Adaptive Microphone-array for NOise Reduction) was proposed as an adaptive beamformer for capturing the desired distant signals in noisy environments by Y. Kaneda and J. Ohga (see IEEE Trans. Acoust. Speech Sig. Process., vol.ASSP-34, no.6, p.1391-1400, 1986). Although the AMNOR has been proven effective, it can be further improved if we know the spectrum characteristics of the desired distant signals in advance. Regarding speech as a desired distant signal, we have designed an AMNOR based on the average speech spectrum. We particularly focus on the performance of the proposed AMNOR for distant-talking speech capture and recognition. Evaluation experiments in real acoustic environments confirm that ASR (automatic speech recognition) performance in noisy environments is improved by 5-10% using our AMNOR. In addition, the proposed AMNOR provides better noise reduction performance than that of the conventional AMNOR.

Distant-talking speech recognition in noisy environments is indispensable for self-moving robots or tele-conference systems. However, background noise and room reverberations seriously degrade the sound-capture quality in real acoustic environments. A microphone array is an ideal candidate as an effective method for capturing distant-talking speech. AMNOR (adaptive microphone-array for noise reduction) was proposed as an adaptive beamformer for capturing the desired distant signals in noisy environments by Kaneda et al. Although the AMNOR has been proven effective, it can be further improved if we know spectrum characteristics of the desired distant signals in advance. Therefore, we regarded speech as a desired distant signal and designed an AMNOR based on the average speech spectrum. In this paper, we particularly focused on the performance of AMNOR based on the average speech spectrum for distant-talking speech capture and recognition. As a result of evaluation experiments in real acoustic environments, we confirmed that the ASR (automatic speech recognition) performance was improved 5-10% by using AMNOR based on the average speech spectrum in noisy environments. In addition, the proposed AMNOR provides better noise reduction performance than that of conventional AMNOR.