This paper investigates visual attention control using the presentation of directional flow stimulus to peripheral vision. Peripheral vision is known to have a superior motion-perception capability. Since central vision is usually used for a primary visual task, it would be quite useful if we could control one's attention by providing assistive information through peripheral motion cues without interfering with the primary task. To evaluate the effectiveness of the proposed method, we conducted experiments on rapid target recognition and visual search tasks under peripheral stimulation conditions. As a result, in the target recognition task, we confirmed that the position with the highest recognition score corresponds to the direction of the presented flow stimuli. In a visual search task, response time decreases when the target position and flow direction match. Furthermore, such matching allows the subjects to more quickly learn how to use the presented information in their search task. Subjective evaluation by questionnaire also demonstrates the intuitiveness and helpfulness of the proposed method. These results support the effectiveness of attention control and visual search assistance using the presentation of directional flow stimuli to peripheral vision.

In this paper, we experimentally examine the relationship between visual cognition difficulty and target-tracking eye movements, which recorded during moving target cognition. Generally, such eye movements are observed when humans perceive a moving object and they vary widely due to many factors, such as target shape, backgrounds, illumination conditions, and so on. Several systems have been proposed for estimating human cognition based on gaze movements. However, since most of them employ simple thresholding techniques to classify the states of cognition, their classification performance remain insufficient. This research clarifies the relationship between multiple visual conditions and target-tracking eye movements to enhance the classification performance. We found that we can address a variety of target and background factors from the perspective of visual cognition difficulty for the targets. Observed gaze movement properties and the difficulty of target visual cognition have a linear relationship, suggesting the possibility of more precise estimation of human's target cognition based on them.

This work introduces a 3D visibility model for comfortable autonomous vehicles. The model computes a visibility index based on the pose of the wheelchair within the environment. We correlate this index with human navigational comfort (discomfort) and we discuss the importance of modeling visibility to improve human riding comfort. The proposed approach models the 3D visual field of view combined with a two-layered environmental representation. The field of view is modeled with information from the pose of the robot, a 3D laser sensor and a two-layered environmental representation composed of a 3D geometric map with traversale area information. Human navigational discomfort was extracted from participants riding the autonomous wheelchair. Results show that there is fair correlation between poor visibility locations (e.g., blind corners) and human discomfort. The approach can model places with identical traversable characteristics but different visibility and it differentiates visibility characteristics according to traveling direction.

This work proposes a model for human habituation while riding a robotic wheelchair. We present and describe the concept of human navigational habituation which we define as the human habituation to repetitively riding a robotic wheelchair. The approach models habituation in terms of preferred linear velocity based on the experience of riding a wheelchair. We argue that preferred velocity changes as the human gets used to riding on the wheelchair. Inexperienced users initially prefer to ride at a slow moderate pace, however the longer they ride they prefer to speed up to a certain comfort level and find initial slower velocities to be tediously “too slow” for their experience level. The proposed habituation model provides passenger preferred velocity based on experience. Human biological measurements, galvanic skin conductance, and participant feedback demonstrate the preference for habituation velocity control over fixed velocity control. To our knowledge habituation modeling is new in the field of autonomous navigation and robotics.

Utterances of five Japanese infants and their parents were recorded longitudinally and used to develop an infant speech database. This database was used to analyze the voicedsegment ratio to investigate the developmental changes in utterances produced by infants and parents. The voicedsegment ratio is the ratio of the summed duration of a voiced segment to the total utterance duration. The analyses showed that the ratio tends to increase in an infant's utterances before the infant starts to produce 2-word sentences. The analyses also showed that, at the same stage, the ratio is higher in parents' infant-directed speech than in parents' adult-directed speech. These results suggest that the voiced-segment ratio reflects the development of an infant's utterance ability, and that a higher voiced-segment ratio is one of the characteristics of infant-directed speech.

Isolated-monosyllable listening tests are widely used in Japan to assess personal hearing ability. However, word intelligibility tests could be more suitable for measuring hearing ability. We developed new word lists with controlled word familiarity and phonetic balance. Listening test results showed slight differences in intelligibility scores among word lists within the same word familiarity rank. We examined whether such variation can be compensated by changing the signal to noise ratio (SNR) of each word to equalize intelligibility scores of word lists in identical word familiarity ranks. Results of listening tests indicated that a 1.0 difference in word familiarity was approximately equivalent to a 2.0 dB difference in SNR. Based on the results, we developed and tested a method of compensating for the difference caused by word familiarity by controlling SNR. We confirmed that this compensation method was effective in equalizing the intelligibility score of each word list.

In speaker verification, two independent stochastic models, i.e. a client model and a non-client (world) model, are generally used to verify the claimed identity using a likelihood ratio score. This paper investigates a variant of this approach based on a common hidden process for both models. In this framework, both models share the same topology, which is conditioned by the underlying phonetic structure of the utterance. Then, two different output distributions are defined corresponding to the client vs. world hypotheses. Based on this idea, a synchronous decoding algorithm and the corresponding training algorithm are derived. Our first experiments on the SESP telephone database indicate a slight improvement with respect to a baseline system using independent alignments. Moreover, synchronous alignment offers a reduced complexity during the decoding process. Interesting perspectives can be expected. Keywords : Stochastic Modeling, HMM, Synchronous Alignment, EM algorithm