Automatic speech recognition is an enormously successful application of statistical pattern recognition. Every day, millions of people use applications based on this technology to solve problems that are most naturally accomplished by interacting with machines through speaking. However, the most successful of these applications have continued to be rather limited in scope, because, although useful, speech recognition can be maddeningly unreliable. Dr. Steven Wegmann, of the International Computer Science Institute (ICSI), hopes to understand in a deep, quantitative way, why the methodology used in nearly all speech recognizers is so brittle. His research will have broad impacts on society. For example, pervasive and accurate automatic speech recognition has the potential to transform society in many positive ways, including providing better access to information for those who find it difficult or even impossible to interact with computers using keyboards such as the elderly, the physically disabled, the vision impaired, or the hearing impaired, to improving technologies we use each day that rely on speech recognition technologies, like Siri, to improving literacy.

As a mathematician, Dr. Wegmann's unique perspective uses simulation and novel sampling processes to generate pseudo test data that mimic true data in order to measure recognition performance. The results of such research are startling enough that they should provoke future studies and a reexamination of where to improve statistical models that we use in speech recognition in order to create more robust recognition performance abilities. As a pure mathematician, Dr. Wegmann uses complex theoretical tools to create solutions for real-world applications. Aside from research, he is committed to educating future scientists. As the leader of the Speech Group at ICSI, Dr. Wegmann continues to lead students and academics in revolutionary work in the subfields of diagnostic research, speaker recognition, and spoken keyword search. Through the combination of his research and his work within the academic community, Dr. Wegmann hopes to one day use statistical models to make the field of speech recognition more accessible for communities outside of academia.

Current research includes:

• Diagnostic Research: Dr. Wegmann is looking at why applying deep neural nets to the speech recognition problem has been so successful. He and his team hope to gain insights that will further assist the development of speech recognition technologies.

• Spoken Password Detection: Spoken password detection technologies allow a speaker to verbally submit a password while the computer system determines if the speaker should be allowed access based on its assessment of the password and the speaker's identity. Dr. Wegmann is building a statistical framework that connects recent advances in big data analysis to automatic speaker recognition that he and his team hope will improve spoken password detection.

• Language Models: Dr. Wegmann hopes to understand human performance in relation to the statistical language model problem. He and his team hope to use crowdsourcing and big data analytics to "break the logjam in language model research."