This paper focuses on language modeling with adequate robustness to support different domain tasks.To this end, we propose a hierarchical latent word language model (h-LWLM).The proposed model can be regarded as a generalized form of the standard LWLMs.The key advance is introducing a multiple latent variable space with hierarchical structure.The structure can flexibly take account of linguistic phenomena not present in the training data.This paper details the definition as well as a training method based on layer-wise inference and a practical usage in natural language processing tasks with an approximation technique.Experiments on speech recognition show the effectiveness of h-LWLM in out-of domain tasks.

A spatial sensitivity control method for segregating the sound sources in the same direction by using an acoustic reflector is proposed. Our goal is to clearly pick up the target source at an arbitrary position using a microphone array. Though many methods have been studied for spatial sensitivity control, it is difficult to robustly suppress the power of noise sources in the same direction of the target source in a room. To overcome this problem, we attach a reflector to a microphone array to capture the reflected sounds whose characteristics vary depending on the distance from the array to the source. Assuming that the acoustical properties of the reflector are known e.g., measuring the transfer functions, those reflected sounds can be used as effective clues for segregating the sound sources in the same direction. With the proposed method, a filter for minimizing the output noise power is derived by taking into consideration of the acoustic properties of the reflector. Experiments were conducted in an actual room by using 96 microphones and a large reflector. We confirmed that the spatial sensitivities for segregating the target source at an arbitrary position from noise sources can be achieved by using the proposed method.

We propose a method for achieving sharp directivity by sensing signals in a diffuse acoustic field. Directivity control based on a beamforming method has been studied to make it possible to extract the waveform and location of an identified target source even if there are many noise sources. Sharp directivity can be achieved by minimizing the output noise power of a beamforming filter. However, it is difficult to minimize the output noise power over a broad frequency ranges. Our approach for minimizing the output noise power is to control the spatial properties of the transfer functions and the spatial correlation matrix, by using a reflector that surrounds a microphone array. We investigated the relationships between the output noise power and the structure of the spatial correlation matrix and found that it was possible to minimize the output noise power by sensing diffuse acoustic signals and by designing filters taking the diffuseness of the acoustic field into consideration. In experiments, we observed diffusely reflected signals by placing a truncated-octahedral reflector near a spherical microphone array. We designed filters by using measured transfer functions and confirmed that the proposed method was effective for reducing the output noise power and forming a sharp directivity beamforming filter.

We present a method for estimating the direct-to-reverberant energy ratio (DRR) that uses a direct and reverberant sound spatial correlation matrix model (Hereafter referred to as the spatial correlation model). This model expresses the spatial correlation matrix of an array input signal as two spatial correlation matrices, one for direct sound and one for reverberation. The direct sound propagates from the direction of the sound source but the reverberation arrives from every direction uniformly. The DRR is calculated from the power spectra of the direct sound and reverberation that are estimated from the spatial correlation matrix of the measured signal using the spatial correlation model. The results of experiment and simulation confirm that the proposed method gives mostly correct DRR estimates unless the sound source is far from the microphone array, in which circumstance the direct sound picked up by the microphone array is very small. The method was also evaluated using various scales in simulated and actual acoustical environments, and its limitations revealed. We estimated the sound source distance using a small microphone array, which is an example of application of the proposed DRR estimation method.

A new approach for estimating the direct-to-reverberant energy ratio (DRR) using a microphone array is proposed. The method is based on a model of a spatial correlation matrix that segregates direct sound and reverberation. It estimates DRR from the power spectra of both components, which are derived from the correlation matrix of the observed signal. In experiments performed in simulated and actual reverberant environments, the proposed method mostly succeeded in estimating DRR accurately. We also present speech enhancement using binary masking as an example of an application of the estimated DRR. By utilization of the DRR as a factor to discriminate the distances of speakers, separation of speech signals whose sources were located in the same direction but at different distances was achieved.

We propose a method for estimating the sound source orientation by using the reflection sounds. The sound source orientation is important spatial information for promoting communication using teleconference systems. We assume that the observed signals captured using several microphones in a reverberant room are used for estimating the sound source orientation. Since the power of each reflection sound depends on the sound source orientation, the transfer functions between a sound source and multiple microphones are varied corresponding to the sound source orientation. We found that the eigenspace of spatial correlation constructed from the observed signals has a characteristic shape corresponding to the sound source orientation. We also proposed an efficient method for estimating the sound source orientation by matching the eigenspace of observed signals with pre-learned eigenspace models for every sound source orientation. In numerical experiments, we obtained about 80% accuracy. We confirmed the effectiveness of the proposed method.

A dereverberation technique is developed to provide an alternative means of reducing reverberation in speech signals. The conventional MINT (the multiple-input/output inverse-filtering theorem) method uses the room impulse responses to calculate the inverse filters, so it cannot recover speech signals in practice, where the room impulse responses are unknown in advance. Our method blindly estimates the inverse filters by computing the correlation matrix between input signals that can be observed, instead of room impulse responses. We also combine the inverse filtering with modified spectral subtraction against the estimation error of inverse filters used in the field. The performance of the proposed method is demonstrated using actual room impulse responses

Stereo echo cancellation has been achieved and used in daily teleconferencing. To overcome the non-uniqueness problem, a stereo shaker is introduced in eight frequency bands and adjusted so as to be inaudible and not affect stereo perception. A due-filter control system including a continually running adaptive filter and a fixed filter is used for double-talk control. A second-order stereo projection algorithm is used in the adaptive filter. A stereo voice switch is also included. This stereo echo canceller was tested in two-way conversation in a conference room, and the strength of the stereo shaker was subjectively adjusted. A misalignment of 20 dB was obtained in the teleconferencing environment, and changing the talker's position in the transmission room did not affect the cancellation. This echo canceller is now used daily in a high-presence teleconferencing system and has been demonstrated to more than 300 attendees.