This thesis explores the treatment of temporal information in Automated Speech Recognition. It reviews the study of time in speech perception and concludes that while some temporal information in the speech signal is of crucial value in the speech decoding process not all temporal information is relevant to decoding. We then review the representation of temporal information in the main automated recognition techniques: Hidden Markov Models and Artificial Neural Networks. We find that both techniques have difficulty representing the type of temporal information that is phonetically or phonologically significant in the speech signal. In an attempt to improve this situation we explore the problem of representation of temporal information in the acoustic vectors commonly used to encode the speech acoustic signal in the front-ends of speech recognition systems. We attempt, where possible, to let the signal provide the temporal structure rather than imposing a fixed, clock-based timing framework. We develop a novel acoustic temporal parameter (the Parameter Similarity Length), a measure of temporal stability, that is tested against the time derivatives of acoustic parameters conventionally used in acoustic vectors.

This study addresses the usefulness of elicited imitation (EI) and automated speech recognition (ASR) as a tool for second language acquisition (SLA) research by evaluating differences among learners of Japanese. The findings indicate that the EI and ASR grading system used in this study was able to differentiate between beginning- and advanced-level learners as well as instructed and self-instructed learners. No significant difference was found between self-instructed learners with and without post-mission instruction. The procedure, reliability and validity of the ASR-based computerized EI are discussed. Results and discussion will provide insights regarding different types of second language (L2) development, the effects of instruction, implications for teaching, as well as limitations of the EI and ASR grading system.

Since the HITECH (Health Information Technology for Economic and Clinical Health) Act was enacted, healthcare providers are required to achieve “Meaningful Use.” CPOE (Clinical Provider Order Entry), is one such requirement. Many providers prefer to dictate their orders rather than typing them. Medical vocabulary is wrought with its own terminology and department-specific acronyms, and many ASR (Automated Speech Recognition) systems are not trained to interpret this language. The purpose of this thesis research was to investigate the use and effectiveness of ASR in the healthcare industry. Multiple hospitals and multiple clinicians agreed to be followed through their use of an ASR system to enter patient data into the record. As a result of this research, the effectiveness and use of the ASR was examined, and multiple issues with the use and accuracy of the system were uncovered.