PhD Data Science Intern (Text-to-Speech) at Vail Systems, Inc.
PhD Data Science Internship: Text-to-Speech (Summer 2020)
Vail Systems Company Profile
The human voice is capable of conveying nuances and meaning that just can't be expressed through clicks and chat messages. For this reason, voice interactions have always had a special power to shape our perceptions and experiences. At Vail, we believe in the unique power of voice interactions to create more expressive and efficient interpersonal interactions. Our experts work with Fortune 500 companies to help them serve their customers more effectively through the use of various voice technologies. From basic network services, to state-of-the-art IP telephony, to cutting edge real-time analytics, Vail technology makes millions of voice interactions better every day.
Designing emotionally sentient agents is challenging. At Vail, we develop models to process and analyze large amounts of natural language data that affect dynamic, real-time communications in various domains. Systems that respond appropriately to social and emotional cues are considered engaging and trust worthy. Currently, such systems rely on speech synthesis technology, such as text-to-speech. The quality of such text-to-speech systems, crucial for dynamic, real time communications, may be judged by their similarity to the human voice and by their ability to be easily understood.
Vail Systems is looking for summer interns who are enrolled in a Ph.D. program in computational linguistics, computer science, or related programs to work with us on some of the challenges such systems present. The student must be conducting research in NLP and should be able to understand, codify and extract meaning from multiple communication modalities (speech, text), and build new or improve existing models in affective computing using state-of-the-art machine learning techniques. The student will work with a team of researchers to gain insight from trying new machine learning approaches, and will work with practitioners to transition the research to a proof-of-concept.