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Audio and video sensory-perception channels interact in animals and humans. Integrated audio-visual processing may abstract independent audio
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and visual scene features to form consistent abstractions for <Scene/> level perception. Joint audio-visual ASR fuses features for audio-visual automatic speech recognition (AV-ASR [266]). Fusion may be based on either feature fusion or decision fusion. With feature fusion, one classi er concatenates features to enhance audio features, for example, at the subphonetic level. With decision fusion, features are concatenated, but phone, word, or utterance level decisions may be affected. Any video assistance improves word error rates as a function of SNR, a key metric in informal AACR dialog in complicated acoustic settings. Noisy environments bene t most from the video cues, amounting to about a 7 dB equivalent improvement and reducing error rates from over 50% to more like 30% in a 5 dB SNR, which is perceived as pretty noisy. Lipreading [266] applies to both noisy speech recognition and to reliably recognizing and tracking the user, protecting the user s private information. The lipreading process begins with the detection of the face. The algorithm then nds the lips and estimates whether the person is talking or not to assist in segmenting the speech signal, differentiating foreground from background speech. Potamianos et al. [266] classify visual features for automatic speech reading as based on either (1) video pixels (e.g., appearance), (2) lip contour (or shape), or (3) a combination of (1) and (2). In addition, systems with binary optics also could incorporate (4) distance-based features or (5) visual ow-based features, both of which estimate motion parameters in three-space. Not all of the speech articulators are visible to an observer. Therefore, the number of visually distinguishable units is much smaller than the number of phonemes. Visible units are called visemes. Speech-readers and statistical clustering are alternative methods for de ning phoneme viseme mappings. Some visemes are well-de ned, such as the bilabial viseme that maps 42 phonemes into 13 visemes. AV-ASR therefore goes beyond the typical hidden Markov model (HMM) of speech-only ASR to the composite HMM states. AV-ASR illustrates the bene ts of integration across these two sensory-perception domains. Although the computational burden of vision and thus of AV-ASR remains high, the continued optimization of computer vision algorithms and work in ASICs continues to propel us closer to AV-AACR. AV and <Scene/> integration, particularly with language as a domain of action, occurs in robots that simulate human behavior, such as GRACE, the rst winner of the IJCAI Mobile Robot Challenge and Aibo, Sony s toy dog. Commercial robots range from Roomba [267], the autonomous vacuum cleaner, to Asimo, the Humanoid. Each has varying degrees of cognitive capability as illustrated in Figure 10-6. Kazuo Murano has said that the most important technology change for the coming decade will be the robot: The robot will probably be the technology of the 21st century as the automobile was the technology of the 20th century.
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FIGURE 10-6 Asimo Humanoid, Abio Dog Robot (from Sony Corp, AP Photos), and GRACE from CMU.
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It is expected to address many social issues in Japan and the other developed nations coping with rapid demographic change, providing security, and improving the convenience and comfort of daily life [268]. The robot competitions at AAAI and IJCAI include the mobile challenge for a robot to check itself into the conference and present a talk about itself, including answering questions. Such robots have many of the attributes of AACR, including multisensor perception of the scene, reasoning, planning, making decisions, and taking actions. GRACE (Graduate Robot Attending ConferencE), the rst winner of the IJCAI robot challenge, isn t a very attractive robot compared to Sony s Asimo, but she has considerable autonomy. AML is the crucial feature that differentiates GRACE, Abio, and Asimo as cognitive systems from merely arti cially intelligent but preprogrammed alternatives. GRACE is a six-foot-tall, socially oriented autonomous talking robot. It was developed by a team of researchers from Carnegie Mellon, the Naval Research Laboratory, Metrica, Inc., Northwestern University, and Swarthmore College. It successfully completed the mobile robot challenge at the American Association of Arti cial Intelligence (AAAI) national meeting in Edmonton, Alberta, Canada on 31 July 2002 [269]. The U.S. Naval Research Laboratory [270] supplied speech recognition, parsing, multimodal speech and gesture interpretation, and human robot interaction. Metrica provided vision-based gesture recognition. Northwestern contributed speech synthesis, and Swarthmore contributed specialized vision for reading of signs and nametags and recognizing people. Physically, GRACE consists of a wheeled mobility platform and a cage of electronics on top of which is a display that shows GRACE or the PowerPoint slide presentation of itself. Because of the limitations of the speech recognition subsystem, GRACE responds to questions posed by the keyboard.
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