This paper proposes a novel multichannel speech enhancement technique for reverberant rooms that is effective when noise sources are spatially stationary, such as a projector fan noise, an air-conditioner noise, and unwanted speech sources at the back of microphones. Speech enhancement performance of the conventional multichannel Wiener filter (MWF) degrades when the Signal-to-Noise Ratio (SNR) of the current microphone input signal changes from the noise-only period. Furthermore, the MWF structure is computationally inefficient, because the MWF updates the whole spatial beamformer periodically to track switching of the speakers (e.g. turn-taking). In contrast to the MWF, the proposed method reduces noise independently of the SNR. The proposed method has a novel two-stage structure, which reduces noise and distortion of the desired source signal in a cascade manner by using two different beamformers. The first beamformer focuses on noise reduction without any constraint on the desired source, which is insensitive to SNR variation. However, the output signal after the first beamformer is distorted. The second beamformer focuses on distortion reduction of the desired source signal. Theoretically, complete elimination of distortion is assured. Additionally, the proposed method has a computationally efficient structure optimized for spatially stationary noise reduction problems. The first beamformer is updated only when the speech enhancement system is initialized. Only the second beamformer is updated periodically to track switching of the active speaker. The experimental results indicate that the proposed method can reduce spatially stationary noise source signals effectively with less distortion of the desired source signal even in a reverberant conference room.

This paper presents a method to improve field uniformity using two TX antennas in a reverberation chamber with less steps of a stirrer. A mode-stirred reverberation chamber (MSRC) is considered as an alternative to the semi-anechoic chamber for an electromagnetic compatibility test because it provides a large test volume, a statistically uniform field, and a high maximum electric field. To improve field uniformity, we introduce two transmitting antennas for excitation in an MSRC, and predict statistical distribution of the complex reflection coefficients (scattering parameters). To prove the validation of our theory and the reliability of measurement results, three kinds of stirrers with different shape and sizes were fabricated and their efficiencies were measured in an MSRC, and then field uniformities have been investigated for 1–3 GHz frequency within the maximum number of independent samples that stirrers can provide. The measurement results show that the average received power is about 1.5 times as high as when using one transmitting antenna, and field uniformity is improved. Use of two transmitting antennas in an MSRC is regarded as a useful method to improve field uniformity at less stirrer steps, for radiated immunity tests.

In this paper, we show that the covariance-eigenvalue approach converges much faster than using cumulative distribution function (CDF) for determining diversity gain from channel measurements in reverberation chamber. The covariance-eigenvalue approach can be used for arbitrary multi-port antennas, but it is limited to Maximum Ratio Combining (MRC).

We propose a blind dereverberation method based on spectral subtraction using a multi-channel least mean squares (MCLMS) algorithm for distant-talking speech recognition. In a distant-talking environment, the channel impulse response is longer than the short-term spectral analysis window. By treating the late reverberation as additive noise, a noise reduction technique based on spectral subtraction was proposed to estimate the power spectrum of the clean speech using power spectra of the distorted speech and the unknown impulse responses. To estimate the power spectra of the impulse responses, a variable step-size unconstrained MCLMS (VSS-UMCLMS) algorithm for identifying the impulse responses in a time domain is extended to a frequency domain. To reduce the effect of the estimation error of the channel impulse response, we normalize the early reverberation by cepstral mean normalization (CMN) instead of spectral subtraction using the estimated impulse response. Furthermore, our proposed method is combined with conventional delay-and-sum beamforming. We conducted recognition experiments on a distorted speech signal simulated by convolving multi-channel impulse responses with clean speech. The proposed method achieved a relative error reduction rate of 22.4% in relation to conventional CMN. By combining the proposed method with beamforming, a relative error reduction rate of 24.5% in relation to the conventional CMN with beamforming was achieved using only an isolated word (with duration of about 0.6 s) to estimate the spectrum of the impulse response.

This letter presents the field uniformity characteristics of a triangular prism reverberation chamber. A reverberation chamber that generally uses a stirrer to create a uniform electric field inside is an alternative to the semi-anechoic chamber for an electromagnetic compatibility test. To overcome the size and maintenance problems of a stirrer, we propose to replace it with a Quadratic Residue Diffuser which is commonly used in acoustics. To confirm that the diffuser is a valid alternative to the stirrer, a diffuser and an equilateral triangular prism reverberation chamber are designed and fabricated for 2.3-3.0 GHz operation. To investigate the field uniformity characteristics by varying the location of the transmitting antenna, both simulation and measurement in the triangular prism reverberation chamber were also done at its two positions, respectively. A commercial program XFDTD 6.2, engaging the finite difference time domain (FDTD), is used for simulation and a cumulative probability distribution, which the IEC 61000-4-21 recommends, is used to evaluate the field uniformity. Both simulation and measurement results show that the field uniformity in the chamber satisfies the international standard requirement of 6 dB tolerance and 3dB standard deviation, which means that a diffuser can be substituted for a stirrer.

This paper presents a method of expanding the operating frequency band of a Reverberating TEM Cell (RTC) for electromagnetic compatibility (EMC) testing. To expand the operating frequency band of an RTC, this paper places a wire septum inside the cell instead of a solid septum. The maximum usable frequency (MUF) for TEM cell operation and the lowest usable frequency (LUF) for reverberating chamber operation with the wire septum are studied and compared with a conventional solid septum. The E field strengths inside the RTC are measured and evaluated. The measurement results show that the RTC with the wire septum have similar MUF to the RTC with a solid septum at TEM mode, but have much lower LUF at a reverberating mode, which proves that the operating frequency band of the RTC can be expanded by using the wire septum.

This letter presents results showing improved field uniformity in a reverberation chamber using quadratic residue diffusers. The optimal occupying ratio of the diffusers on one side wall of the chamber is presented. A reverberation chamber is an alternative to the semi-anechoic chamber, which is widely used for the analysis and measurement of electromagnetic interference and immunity. To analyze the field characteristics, quadratic residue diffusers were designed for the 1-3 GHz frequency band, and the FDTD method was used. At 1-3 GHz, the standard deviation of the test volume in the reverberation chamber was investigated. The reverberation chamber had good field uniformity when quadratic residue diffusers occupy 37.5-50% of one side wall of the reverberation chamber; the field uniformity saturated at the diffuser occupancy rate of 75%.

The extraction of verb synonyms is a key technology to build a verb dictionary as a language resource. This paper presents a co-clustering-based verb synonym extraction approach that increases the number of extracted meanings of polysemous verbs from a large text corpus. For verb synonym extraction with a clustering approach dealing with polysemous verbs can be one problem issue because each polysemous verb should be categorized into different clusters depending on each meaning; thus there is a high possibility of failing to extract some of the meanings of polysemous verbs. Our proposed approach can extract the different meanings of polysemous verbs by recursively eliminating the extracted clusters from the initial data set. The experimental results of verb synonym extraction show that the proposed approach increases the correct verb clusters by about 50% with a 0.9% increase in precision and a 1.5% increase in recall over the previous approach.

This paper presents the field uniformity characteristics in a triangular prism reverberation chamber that can be substituted for an open area test site or an anechoic chamber to measure electromagnetic interference. To improve size problems of a stirrer that is an official unit to generate a uniform field in the reverberation chamber, we suggest a diffuser of Quadratic Residue Sequence method. To validate the substitution of a diffuser for a stirrer, a diffuser is designed for 1-3 GHz, and three types of equilateral triangular prism reverberation chambers are modeled. Afterwards, the field distributions in these three reverberation chambers are both simulated and tested. Using XFDTD 6.2 of finite difference time domain method, field deviations of each structure are simulated and compared to each other. An evaluation of field uniformity is done by cumulative probability distribution which is specified in the IEC 61000-4-21. The result shows that the field uniformity in the chamber is within 6 dB tolerance and also within 3 dB standard deviation, which means a diffuser can satisfy the requirement of international standards.

Transmission delay in audio communications is a well-known obstacle to achieving smooth communication. However, it is not known what kinds of effects are caused by small delays. We hypothesized that the small delay in the listener's responses disturbs the speaker's "verbal conditioning," where the verbal behavior of the speaker varies in accordance with the listener's responses. We examined whether the small delays in the listener's responses disturb the speaker's verbal conditioning using an artificial-grammar learning task. The results suggested that a 300-ms delay disturbed the participants' verbal conditioning although they were not adequately aware of the delay.

Speech dereverberation is one of the most difficult tasks in acoustic signal processing. Of the various problems involved in this task, this paper highlights "over-whitening," which flattens the characteristics of recovered speech. This distortion sometimes happens when inverse filters are directly calculated from microphone signals. This paper reviews two studies related to this problem. The first study shows the possibility of compensating for such over-whitening to achieve precise speech-dereverberation. The second study presents a new approach for approximating the original speech by removing the effect of late reflections from observed reverberant speech.

This paper discusses the design of configurations of videophone equipment aimed at online sign interpretation. We classified interpretation services into three types of situations: on-site interpretation, partial online interpretation, and full online interpretation. For each situation, the spatial configurations of the equipment are considered keeping the issue of nonverbal signals in mind. Simulation experiments of sign interpretation were performed using these spatial configurations and the qualities of the configurations were assessed. The preferred configurations had the common characteristics that the hearing subject could see the face of his/her principal conversation partner, that is, the deaf subject. The results imply that hearing people who do not understand sign language utilize nonverbal signals for facilitating interpreter-mediated conversation.

A novel technique for calibrating antenna gain in random electric fields is presented. Our technique exploits the statistical characteristics of complex electric fields in multipath environments that change with time. A reverberation chamber, consisting of a shielded enclosure equipped with mechanical stirrers, was used to determine this technique's validity experimentally. Such chambers can create, using rotating stirrers, multipath environments that change with time. A comparison of the results obtained in a reverberation chamber and those obtained by the conventional method in an anechoic chamber demonstrates the efficacy of this technique.

In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicate-argument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.

We have attempted to recognize reverberant speech using a novel speech recognition system that depends on not only the spectral envelope and amplitude modulation but also frequency modulation. Most of the features used by modern speech recognition systems, such as MFCC, PLP, and TRAPS, are derived from the energy envelopes of narrowband signals by discarding the information in the carrier signals. However, some experiments show that apart from the spectral/time envelope and its modulation, the information on the zero-crossing points of the carrier signals also plays a significant role in human speech recognition. In realistic environments, a feature that depends on the limited properties of the signal may easily be corrupted. In order to utilize an automatic speech recognizer in an unknown environment, using the information obtained from other signal properties and combining them is important to minimize the effects of the environment. In this paper, we propose a method to analyze carrier signals that are discarded in most of the speech recognition systems. Our system consists of two nonlinear discriminant analyzers that use multilayer perceptrons. One of the nonlinear discriminant analyzers is HATS, which can capture the amplitude modulation of narrowband signals efficiently. The other nonlinear discriminant analyzer is a pseudo-instantaneous frequency analyzer proposed in this paper. This analyzer can capture the frequency modulation of narrowband signals efficiently. The combination of these two analyzers is performed by the method based on the entropy of the feature introduced by Okawa et al. In this paper, in Sect. 2, we first introduce pseudo-instantaneous frequencies to capture a property of the carrier signal. The previous AM analysis method are described in Sect. 3. The proposed system is described in Sect. 4. The experimental setup is presented in Sect. 5, and the results are discussed in Sect. 6. We evaluate the performance of the proposed method by continuous digit recognition of reverberant speech. The proposed system exhibits considerable improvement with regard to the MFCC feature extraction system.

Development of an artificial reverberator for low-memory requirements is an issue of importance in applications such as mobile multimedia devices. One possibility is to use an All-Pass Filter (APF), which is embedded in the feedback loop of the comb filter network. In this paper, we propose a reverberator employing time-varying APFs to increase the reverberation performance. By changing the gain of the APF, we can increase the number of frequency peaks perceptually. Thus, the resulting reverberation sounds much more natural, even with less memory, than the conventional approach. In this paper, we perform theoretical and perceptual analyses of artificial reverberators employing time-varying APF. Through the analyses, we derive the degree of phase variation of the APF that is perceptually acceptable. Based on the analyses, we propose a method of designing artificial reverberators associated with the time-varying APFs. Through subjective tests, it is shown that the proposed method is capable of providing perceptually comparable sound quality to the conventional methods even though it uses less memory.

This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering 'why' question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least three main problems involved in boundaries identification: the implicit boundary delimiter, the nonadjacent cause-consequence, and the effect surrounded by causes. This research proposes using verb-pair rules learnt by comparing the Naïve Bayes classifier (NB) and Support Vector Machine (SVM) to identify causality EDUs in Thai agricultural and health news domains. The boundary identification problems are solved by utilizing verb-pair rules, Centering Theory and cue phrase set. The reason for emphasizing on using verbs to extract causality is that they explicitly make, in a certain way, the consequent events of cause-effect, e.g. 'Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. The outcome of the proposed methodology shown that the verb-pair rules extracted from NB outperform those extracted from SVM when the corpus contains high occurence of each verb, while the results from SVM is better than NB when the corpus contains less occurence of each verb. The verb-pair rules extracted from NB for causality extraction has the highest precision (0.88) with the recall of 0.75 from the plant disease corpus whereas from SVM has the highest precision (0.89) with the recall of 0.76 from bird flu news. For boundary determination, our methodology can handle very well with approximate 96% accuracy. In addition, the extracted causality results from this research can be generalized as laws in the Inductive-Statistical theory of Hempel's explanation theory, which will be useful for QA and reasoning.

It is well known that speech captured in a room by distant microphones suffers from distortions caused by reverberation. These distortions may seriously damage both speech characteristics and intelligibility, and consequently be harmful to many speech applications. To solve this problem, we proposed a dereverberation algorithm based on multi-channel linear prediction. The method is as follows. First we calculate prediction filters that cancel out the room reverberation but also degrade speech characteristics by causing excessive whitening of the speech. Then, we evaluate the prediction-filter degradation to compensate for the excessive whitening. As the reverberation lengthens, the compensation performance becomes worse due to computational accuracy problems. In this paper, we propose a new computation that may improve compensation accuracy when dealing with long reverberation.

We describe attempts to have robots behave as embodied knowledge media that will permit knowledge to be communicated through embodied interactions in the real world. The key issue here is to give robots the ability to associate interactions with information content while interacting with a communication partner. Toward this end, we present two contributions in this paper. The first concerns the formation and maintenance of joint intention, which is needed to sustain the communication of knowledge between humans and robots. We describe an architecture consisting of multiple layers that enables interaction with people at different speeds. We propose the use of an affordance-based method for fast interactions. For medium-speed interactions, we propose basing control on an entrainment mechanism. For slow interactions, we propose employing defeasible interaction patterns based on probabilistic reasoning. The second contribution is concerned with the design and implementation of a robot that can listen to a human instructor to elicit knowledge, and present the content of this knowledge to a person who needs it in an appropriate situation. In addition, we discuss future research agenda toward achieving robots serving as embodied knowledge media, and fit the robots-as-embodied-knowledge-media view in a larger perspective of Conversational Informatics.

This paper describes a hands-free speech recognition technique based on acoustic model adaptation to reverberant speech. In hands-free speech recognition, the recognition accuracy is degraded by reverberation, since each segment of speech is affected by the reflection energy of the preceding segment. To compensate for the reflection signal we introduce a frame-by-frame adaptation method adding the reflection signal to the means of the acoustic model. The reflection signal is approximated by a first-order linear prediction from the observation signal at the preceding frame, and the linear prediction coefficient is estimated with a maximum likelihood method by using the EM algorithm, which maximizes the likelihood of the adaptation data. Its effectiveness is confirmed by word recognition experiments on reverberant speech.