Industrial Engineering and Operations Research (ABSTRACT)Automatic speech recog n ition systems have at last advanced to the state that they are now a feasible alternative for human-machine communication in selected appli ...cations. As such, research efforts are now beginning to focus on characteristics of the human, the recognition device, and the interface which optimize the system performance, rather than the previous trend of determining factors affecting recognizer performance alone. This study investigated two characteristics of the recognition device, the accuracy level at which it recognizes speech, and the vocabulary size of the recognizer as a percent of task vocabulary size to determine their effects on system per:ormance. In addition, the study considered one characteristic of the user, age. Briefly, subjects performed a data entry task under each of the treatment conditions. Task completion time and the number of errors remaining at the end of each session were recorded. After each session, subjects rated the recognition device used as to its acceptability for the task.The accuracy level at which the recognizer was performing significantly influenced the task completion time as well as the user's acceptability ratings, but had only a small effect on the number of errors left uncorrected. The available vocabulary size also significantly affected the task completion time; however its effect on the final error rate and on the acceptability ratings was negligible. The age of the subject was also found to influence both objective and subjective measures. Older subjects in general required longer times to complete the tasks; however, they consistently rated the speech input systems more favorably than the younger subjects. AcknowledgementsThe author wishes to express appreciation to Dr. Robert D. Dryden for the support and guidance he provided as committee chairman throughout the course of this research. Special thanks are also due to committee members Beverly H. Williges for the direction and expertise she provided, and Professor Paul T. Kemmerling for his advice and encouragement. I also wish to thank Calvin L. Selig for the development of the speech recognition system simulation software.On a more personal note, I would like to thank my parents for a lifetime of support and encouragement. And finally, I would like to thank my husband John, whose patience and encouragement have helped make the past two years an enjoyable as well as rewarding experience. Acknowledgements Iv List of Tables IntroductionAutomatic speech recognition refers to the ability of a machine to discriminate spoken utterances. The first such devices were devel~ped in the mid 1950'st and significant progress has been made within the past decade. It has long been known that unconstrained speech is the fastest, most efficient means for two people to communicate (Chapanis, 1975). Now, applying spoken communication to human-machine systems is proving to be an efficient alternative for many applications. Such applications includ...

As science and technology become more sophisticated and with the rapid computation capabilities of the modern computer available, it becomes both possible and economically feasible to scientifically study man and his interaction with his working environment. It is now possible for a person seeking employment to expect and obtain a position which will not be unnecessarily hazardous to his immediate health or have detrimental effects over the long run. Manual materials handling is the contributor of over 400,000 back injuries suffered in the U.S. each year. This research is directed at determining the appropriate operator variables to measure for predicting the permissible weight of lift for three ranges of lift: floor to knuckle height, knuckle height to shoulder height, and shoulder height to reach height. A modified psychophysical procedure was used during which the subjects were instructed to adjust the weight in a tote box to the maximum weight they could lift repetitively without excessive strain or fatique. The task consisted of lifting loads under different conditions of task variables, namely, height of lift, frequency of lift, and load size. Industrial workers as well as students of both sexes were used as subjects. Based on the data obtained, the lifting capacity of the worker was determined for the different ranges of lift. In addition, predictive models were developed based on the operator variables and the task variables investigated.

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University–industry–government partnerships have become extremely significant and important technology transfer instruments in the USA. These partnerships have also created challenges and opportunities that necessitate a new ‘culture’ or ‘mindset’ for the collaborating partners. The early identification and awareness of barriers that may jeopardize an effective collaboration has presented itself as one of the major challenges. The paper begins with a discussion of major technology transfer issues and views collaboration from the perspective of industry, government and universities. Questions such as ‘What are the primary concerns facing all partners?’ and ‘What can be done to effect change in the mindset of partners?’ are addressed in the light of case studies. The paper also describes six innovative university–industry–government collaboration models initiated in the USA that include both domestic and international ventures under implementation.

Industrial Engineering and Operations Research (ABSTRACT)Automatic speech recog n ition systems have at last advanced to the state that they are now a feasible alternative for human-machine communication in selected appli ...cations. As such, research efforts are now beginning to focus on characteristics of the human, the recognition device, and the interface which optimize the system performance, rather than the previous trend of determining factors affecting recognizer performance alone. This study investigated two characteristics of the recognition device, the accuracy level at which it recognizes speech, and the vocabulary size of the recognizer as a percent of task vocabulary size to determine their effects on system per:ormance. In addition, the study considered one characteristic of the user, age. Briefly, subjects performed a data entry task under each of the treatment conditions. Task completion time and the number of errors remaining at the end of each session were recorded. After each session, subjects rated the recognition device used as to its acceptability for the task.The accuracy level at which the recognizer was performing significantly influenced the task completion time as well as the user's acceptability ratings, but had only a small effect on the number of errors left uncorrected. The available vocabulary size also significantly affected the task completion time; however its effect on the final error rate and on the acceptability ratings was negligible. The age of the subject was also found to influence both objective and subjective measures. Older subjects in general required longer times to complete the tasks; however, they consistently rated the speech input systems more favorably than the younger subjects. AcknowledgementsThe author wishes to express appreciation to Dr. Robert D. Dryden for the support and guidance he provided as committee chairman throughout the course of this research. Special thanks are also due to committee members Beverly H. Williges for the direction and expertise she provided, and Professor Paul T. Kemmerling for his advice and encouragement. I also wish to thank Calvin L. Selig for the development of the speech recognition system simulation software.On a more personal note, I would like to thank my parents for a lifetime of support and encouragement. And finally, I would like to thank my husband John, whose patience and encouragement have helped make the past two years an enjoyable as well as rewarding experience. Acknowledgements Iv List of Tables IntroductionAutomatic speech recognition refers to the ability of a machine to discriminate spoken utterances. The first such devices were devel~ped in the mid 1950'st and significant progress has been made within the past decade. It has long been known that unconstrained speech is the fastest, most efficient means for two people to communicate (Chapanis, 1975). Now, applying spoken communication to human-machine systems is proving to be an efficient alternative for many applications. Such applications includ...