Create and release your Profile on Zintellect – Postdoctoral applicants must create an account and complete a profile in the on-line application system. Please note: your resume/CV may not exceed 2 pages.
Complete your application – Enter the rest of the information required for the IC Postdoc Program Research Opportunity. The application itself contains detailed instructions for each one of these components: availability, citizenship, transcripts, dissertation abstract, publication and presentation plan, and information about your Research Advisor co-applicant.
Additional information about the IC Postdoctoral Research Fellowship Program is available on the program website located at: https://orise.orau.gov/icpostdoc/index.html.
If you have questions, send an email to ICPostdoc@orau.org. Please include the reference code for this opportunity in your email.
Research Topic Description, including Problem Statement:
Recent deep learning developments have produced dramatic improvements in the performance of speech recognition technologies. Many advancements are due to end-to-end (E2E) neural network models, which optimize a direct mapping from audio data directly to text information with a single network. However, the supervised manner in which these E2E models are trained does not produce state-of-the-art performance results in adverse conditions, such as noisy audio environments and low-resource language scenarios.
With this project, we aim to identify methods to improve E2E speech recognition performance robustness for noisy audio environments and low-resource language scenarios.
Relevance to the Intelligence Community (IC):
Speech recognition is a fundamental Human Language Technology (HLT) capability that provides audio content triage capabilities. New state-of-the-art E2E neural network approaches remove onerous large, labeled data requirements needed to develop a speech recognition capability for a language of interest; however, these gains are at the expense of lower performance results in adverse audio environments and low-resource scenarios. The work described above would identify solutions to these issues which are critical to maintaining a competitive edge in language analysis capabilities.
Key Words: #Speech Recognition, #Multilingual, #Deep Learning, #Neural Network, #End-to-end, #Low Resource, #Noise Robustness