Radio Frequency Identification (RFID) provides various opportunities to increase the productivity of retail business. In this paper, we describe a usability evaluation study for an RFID-based location tracking application, called Beep-To-The-Box (BTTB). The experiment was conducted in a simulated retail store to gain in-depth understanding of the usefulness and usability of the prototype in determining visual and audio user interface features. We describe the features of the BTTB, report the experimental results, and discuss insights gained to provide design recommendations for the final product design.

Social television applications have emerged as a potentially valuable convergence of media and communication, but questions remain about the utility and nature of the communication experiences they will provide. We present our study of STV3, an application that adds freeform text and voice chat capabilities to the conventional television-viewing experience. We conducted an in-depth field study of STV3 to understand how friends integrate communication through social television into their lives. Our results reveal users' choices of communication modality, their topics of conversation, and the sense of connectedness that was fostered through their use of STV3. Our findings indicate that participants overwhelmingly preferred text chat to voice chat, and that they often communicated about topics unrelated to the television content.

We examine how ambient displays can augment social television. Social TV 2 is an interactive television solution that incorporates two ambient displays to convey to participants an aggregate view of their friends' current TV-watching status. Social TV 2 also allows users to see which television shows friends and family are watching and send lightweight messages from within the TV-viewing experience. Through a two-week field study we found the ambient displays to be an integral part of the experience. We present the results of our field study with a discussion of the implications for future social systems in the home.

We report on a field evaluation of a prototype social television system (Social TV) that incorporates lightweight messaging as well as ambient awareness of user presence on the system. This evaluation was conducted over a two-week period and involved the participation of ten households. Participants appreciated the ability to see their buddies' presence on the system, the ability to see or suggest the programs they were currently watching, and the ability to send short messages to one another. The presence facilities available in Social TV also allowed participants to learn more about one another's TV viewing habits and preferences, and fostered a sense of connectedness between them. However, they also felt constrained by the limitations of the communication options available to them and demanded free-form text or voice chat to be able to fully express themselves.

A classifier was trained to detect driver inattention using output from typical sensors available on modern vehicles equipped with a Collision Avoidance System (CAS). A driving simulator was used to collect driver and vehicle data from ten subjects during normal driving periods and during periods where drivers looked away from their forward view as they turned their head to look at a detailed image in their blindspot. This data was used to train a 2-state (attentive, inattentive) and 3-state (attentive, inattentive left, inattentive right) classifier. Monitoring of driver attention is typically performed by in-vehicle cameras or specialized sensors but we were able to use simulated CAS sensor output to detect over 80% of driver inattention time segments in the absence of driver monitoring sensors.

Prior attempts to use neural networks to synthesize speech from a phonetic representation have used the neural network to generate a frame of input to a vocoder. As this requires the neural network to compute one output for each frame of speech from the vocoder, this can be computationally expensive. An alternative implementation is to model the speech as a series of gestures, and let the neural network generate parameters describing the transitions of the vocoder parameters during these gestures. Experiments have shown that acceptable speech quality is produced when each gesture is half of a phonetic segment and the transition model is a set of cubic polynomials describing the variation of each vocoder parameter during the gesture. This results in a significant reduction in computational cost.

While neural networks have been employed to handle several different text-to-speech tasks, ours is the first system to use neural networks throughout, for both linguistic and acoustic processing. We divide the text-to-speech task into three subtasks, a linguistic module mapping from text to a linguistic representation, an acoustic module mapping from the linguistic representation to speech, and a video module mapping from the linguistic representation to animated images. The linguistic module employs a letter-to-sound neural network and postlexical neural network. The acoustic module employs a duration neural network and a phonetic neural network. The visual neural network is employed in parallel to the acoustic module to drive a talking head. The use of neural networks that can be retrained on the characteristics of different voices and languages affords our system a degree of adaptability and naturalness heretofore unavailable.

This paper describes the design of a neural network that performs the phonetic-to-acoustic mapping in a speech synthesis system.The use of a time-domain neural network architecture limits discontinuities that occur at phone boundaries.Recurrent data input also helps smooth the output parameter tracks.Independent testing has demonstrated that the voice quality produced by this system compares favorably with speech from existing commercial text-to-speech systems.