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This paper presents an interactive quizmaster robot that can manage a multiparty speech-based quiz game. The basic flow of the quiz game is that (1) the robot reads a question, (2) one or more players answer it, and (3) the robot judges the correctness of the answers. We categorize such speech-based quiz games into school-type interaction and auction-type interaction. The former asks players to say ‘Yes’ to get the right to answer before answering a question and the latter allows players to directly answer a question without any advance notice. To realize such interaction, the robot needs the capability of recognizing utterances from multiple people using its own microphones (i.e. ears), even if those utterances are made simultaneously. To cope with such situations, the robot estimates which player made the fastest utterance and recognizes it by localizing and separating a mixture of audio signals. Experiments were conducted to evaluate the success rates of the fastest player identification and speech recognition. The results showed that the robot could identify the fastest speakers with a success rate of 90.0% more accurately than humans when only one speaker slightly preceded the other speakers. We found that although the success rate of speech recognition for the fastest speakers did not reach that of humans, the robot attains amusing quiz game interaction. [ABSTRACT FROM AUTHOR]

Hose-shaped rescue robots have been developed for searching narrow spaces such as under collapsed buildings. The posture estimation independent of the past history is critical, because conventional inertial-sensor-based posture estimation has two main problems; a cumulative error problem peculiar to inertial sensors, and a sudden posture change problem caused by external forces. For coping with the two problems, we developed a novel posture estimation method by putting an active microphone array, a set of microphones and loudspeakers, on the robot. The method calculates the time difference of arrival (TDOA) of the reference signal emitted from one loudspeaker, and estimates the posture from the distance obtained by TDOA. This concise method still has three problems: (1) external noise, (2) reverberation and reflection, and (3) obstacles. These problems are tackled by (1) TSP signal, (2) GCC-PHAT and a threshold-based onset detection, and (3) rejecting incorrect onset times, respectively. Experiments with simulated sounds and actual recordings demonstrate that the method attains the performance of estimation comparable to that of conventional methods, that is, less than 20 cm of the tip position error. Even without historical data, the method attains the similar performance while conventional methods fail. [ABSTRACT FROM AUTHOR]