Hideki Asoh

Combined approach of array processing and independent component analysis for blind separation of acoustic signals

Two array signal processing techniques are combined with independent component analysis (ICA) to enhance the performance of blind separation of acoustic signals in a reflective environment. The first technique is the subspace method which reduces the effect of room reflection when the system is used in a room. Room reflection is one of the biggest problems in blind source separation (BSS) in acoustic environments. The second technique is a method of solving permutation. For employing the subspace method, ICA must be used in the frequency domain, and precise permutation is necessary for all frequencies. In this method, a physical property of the mixing matrix, i.e., the coherency in adjacent frequencies, is utilized to solve the permutation. The experiments in a meeting room showed that the subspace method improved the rate of automatic speech recognition from 50% to 68% and that the method of solving permutation achieves performance that closely approaches that of the correct permutation, differing by only 4% in recognition rate.

Robust speech interface based on audio and video information fusion for humanoid HRP-2

For cooperative work of robots and humans in the real world, a communicative function based on speech is indispensable for robots. To realize such a function in a noisy real environment, it is essential that robots be able to extract target speech spoken by humans from a mixture of sounds by their own resources. We have developed a method of detecting and extracting speech events based on the fusion of audio and video information. In this method, audio information (sound localization using a microphone array) and video information (human tracking using a camera) are fused by a Bayesian network to enable the detection of speech events. The information of detected speech events is then utilized in sound separation using adaptive beam forming. In this paper, some basic investigations for applying the above system to the humanoid robot HRP-2 are reported. Input devices, namely a microphone array and a camera, were mounted on the head of HRP-2, and acoustic characteristics for sound localization/separation performance were investigated. Also, the human tracking system was improved so that it can be used in a dynamic situation. Finally, overall performance of the system was tested via off-line experiments.

Jijo-2: an office robot that communicates and learns

Describes how the authors have combined speech recognition, dialogue management, and statistical learning procedures to develop Jijo-2; an office robot that can communicate with humans and learn about its environment.

Twitter user profiling based on text and community mining for market analysis

This paper proposes demographic estimation algorithms for profiling Twitter users, based on their tweets and community relationships. Many people post their opinions via social media services such as Twitter. This huge volume of opinions, expressed in real time, has great appeal as a novel marketing application. When automatically extracting these opinions, it is desirable to be able to discriminate discrimination based on user demographics, because the ratio of positive and negative opinions differs depending on demographics such as age, gender, and residence area, all of which are essential for market analysis. In this paper, we propose a hybrid text-based and community-based method for the demographic estimation of Twitter users, where these demographics are estimated by tracking the tweet history and clustering of followers/followees. Our experimental results from 100,000 Twitter users show that the proposed hybrid method improves the accuracy of the text-based method. The proposed method is applicable to various user demographics and is suitable even for users who only tweet infrequently.

Sound source localization and signal separation for office robot "JiJo-2"

A method of sound source localization and source separation for the sound sources in the near-field range is described in this paper. For the sound localization, the subspace method was employed to obtain a high resolution which is necessary for the range estimation with a small-sized array. The modified MVBF with the estimated localization information was used for signal separation. In an experiment conducted in a middle-sized meeting room, 60-70% of word recognition rate was achieved.

Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Data

We propose a novel approach for constructing statistical preference models for context-aware recommender systems. To do so, one of the most important but difficult problems is acquiring sufficient training data in various contexts/situations. Particularly, some situations require a heavy workload to set them up or to collect subjects under those situations. To avoid this, often a large amount of data in a supposed situation is collected, i.e., a situation where the subject pretends/imagines that he/she is in a specific situation. Although there may be difference between the preference in the real situation and the supposed situation, this has not been considered in existing researches. Here, to study the difference, we collected a certain amount of corresponding data. We asked subjects the same question about preference both in the real and the supposed situation. Then we proposed a new model construction method using a difference model constructed from the correspondence data and showed the effectiveness through the experiments.

A combined approach of array processing and independent component analysis for blind separation of acoustic signals

Two array signal processing techniques are combined with independent component analysis to enhance the performance of blind separation of acoustic signals in a reflective environment such as rooms. The first technique is the subspace method which reduces the effect of room reflection. The second technique is a method of solving the permutation, in which the coherency of the mixing matrix in adjacent frequencies is utilized.

Detection and separation of speech event using audio and video information fusion and its application to robust speech interface

A method of detecting speech events in a multiple-sound-source condition using audio and video information is proposed. For detecting speech events, sound localization using a microphone array and human tracking by stereo vision is combined by a Bayesian network. From the inference results of the Bayesian network, information on the time and location of speech events can be known. The information on the detected speech events is then utilized in the robust speech interface. A maximum likelihood adaptive beamformer is employed as a preprocessor of the speech recognizer to separate the speech signal from environmental noise. The coefficients of the beamformer are kept updated based on the information of the speech events. The information on the speech events is also used by the speech recognizer for extracting the speech segment.

Combining probabilistic map and dialog for robust life-long office navigation

A design of mobile robot for robust life-long navigation in office environment is proposed and evaluated. The key idea is combining probabilistic map and dialog with humans for reducing the location uncertainty. Bayesian inference with the map represented by probabilistic automata is used in order to reduce the number of queries and to evaluate the success rate of planned paths. We experimentally implemented the design using a simple Bayesian network with continuous nodes and demonstrated its effectiveness in a real environment.

An event-driven architecture for controlling behaviors of the office conversant mobile robot, Jijo-2

In order for our office conversant robot to take robust actions according to events rising from different kind of sensors, a layered process network architecture based on an event-driven control model is presented. In this architecture, high level modules are freed from time consuming checking of sensors, and are able to follow multiple scenario-driven consistent behaviors, allowing interruption from human and environment. Programs are written as series of event-action pairs, which are also effective to keep reactiveness and modularity high.