System Request Utterance Detection Based on Acoustic and Linguistic Features

Abstract

Robots are now being designed to become a part of the lives of ordinary people in social and home environments, such as a service robot at the office, or a robot serving people at a party (H. G. Okuno, et al., 2002 ) (J. Miura, et al., 2003). One of the key issues for practical use is the development of technologies that allow for user-friendly interfaces. This is because many robots that will be designed to serve people in living rooms or party rooms will be operated by non-expert users, who might not even be capable of operating a computer keyboard. Much research has also been done on the issues of human-robot interaction. For example, in (S. Waldherr, et al., 2000), the gesture interface has been described for the control of a mobile robot, where a camera is used to track a person, and gestures involving arm motions are recognized and used in operating the mobile robot. Speech recognition is one of our most effective communication tools when it comes to a hands-free (human-robot) interface. Most current speech recognition systems are capable of achieving good performance in clean acoustic environments. However, these systems require the user to turn the microphone on/off to capture voices only. Also, in hands-free environments, degradation in speech recognition performance increases significantly because the speech signal may be corrupted by a wide variety of sources, including background noise and reverberation. In order to achieve highly effective speech recognition, in (H. Asoh, et al., 1999), a spoken dialog interface of a mobile robot was introduced, where a microphone array system is used. In actual noisy environments, a robust voice detection algorithm plays an especially important role in speech recognition, and so on because there is a wide variety of sound sources in our daily life, and because the mobile robot is requested to extract only the object signal from all kinds of sounds, including background noise. Most conventional systems use an energyand zero-crossing-based voice detection system (R. Stiefelhagen, et al., 2004). However, the noise-power-based method causes degradation of the detection performance in actual noisy environments. In (T. Takiguchi, et al., 2007), a robust speech/non-speech detection algorithm using AdaBoost, which can achieve extremely high detection rates, has been described. Also, for a hands-free speech interface, it is important to detect commands in spontaneous utterances. Most current speech recognition systems are not capable of discriminating system requests utterances that users talk to a system from human-human conversations. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg