Speech recognition thesis 2012

He received the B. He was a faculty member at Duke Univ. He has served as a technical consultant to industry and the U.

Speech recognition thesis 2012

Speech recognition thesis 2012

Massachusetts Institute of Technology. Massachusetts Institute of Technology Date Issued: Accurate and comprehensive data form the lifeblood of health care. Unfortunately, there is much evidence that current data collection methods sometimes fail.

Our hypothesis is that it should be possible to improve the thoroughness and quality of information gathered through clinical encounters by developing a computer system that a listens to a conversation between a patient and a provider, b uses automatic speech recognition technology to transcribe that conversation to text, c applies natural language processing methods to extract the important clinical facts from the conversation, d presents this information in real time to the participants, permitting correction of errors in understanding, and e organizes those facts into an encounter note that could serve as a first draft of the note produces by the clinician.

In this thesis, we present our attempts to measure the performances of two state-of-the-art automatic speech recognizers ASRs for the task of transcribing clinical conversations, and explore the potential ways of optimizing these software packages for the specific task. In the course of this thesis, we have 1 introduced a new method for quantitatively measuring the difference between two language models and showed that conversational and dictational speech have different underlying language models, 2 measured the perplexity of clinical conversations and dictations and shown that spontaneous speech has a higher perplexity than dictational speech, 3 improved speech recognition accuracy by language adaptation using a conversational corpus, and 4 introduced a fast and simple algorithm for cross talk elimination in two speaker settings.

The certified thesis is available in the Institute Archives and Special Collections.A Historical Perspective of Speech Recognition from CACM on Vimeo.

Research Interests

With the introduction of Apple's Siri and similar voice search services from Google and Microsoft, it is natural to wonder why it has taken so long for voice recognition technology to advance to this level. –In continuous speech, words may not be distinguishable based on their acoustic information alone •First, due to coarticulation, word boundaries are not usually clear.

The Effects of Stimulus Type and Response Condition on Dichotic Speech Recognition Honors Research Thesis Madelyn Stevens The Ohio State University June Project Advisor: Dr. Christina M. Roup, Department of Speech and Hearing Science.

Mar 03,  · The Microsoft Speech SDK adds Automation support to the features of the previous version of the Speech SDK. You can now use the Win32 Speech API (SAPI) to develop speech applications with Visual Basic ®, ECMAScript and other Automation languages.

Noise Robust Voice Activity Detection Pham Chau Khoa Master of Engineering Abstract Voice activity detection (VAD) is a fundamental task in various speech-related appli-cations, such as speech coding, speaker diarization and speech recognition.

It is often defined as the problem of distinguishing speech from silence/noise. The Ig Nobel Prizes were awarded on Thursday night, September 22, at the 26th First Annual Ig Nobel Prize Ceremony, at Harvard's Sanders mtb15.com ceremony was webcast..

REPRODUCTION PRIZE [EGYPT] — The late Ahmed Shafik, for studying the effects of wearing polyester, cotton, or wool trousers on the sex life of rats, and for conducting similar tests with human males.

Speech recognition - Wikipedia