Abstract Dependencies among texts arise when speakers and writers copy manuscripts, cite the scholarly literature, speak from talking points, repost content on social networking platforms, popularize scientific papers for the general public, or in other[…]

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Abstract Traditional automatic speech recognition (ASR) systems are comprised of an acoustic model (AM), a pronunciation model (PM) and a language model (LM), all of which are independently trained, and often manually designed, on different[…]

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Abstract When confronted with the daunting task of transmitting speech information to deaf individuals, one comes quickly to the conclusion that the solution to this problem requires a full-blown theory of speech perception. Because the[…]

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Abstract In a classic social psychology experiment by Fritz Heider and Marianne Simmel (1944), subjects watched a short silent film depicting the motions of two triangles and a circle around a box with a door,[…]

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Abstract Deep learning with large supervised training sets has had significant impact on many research challenges, from speech recognition to machine translation. However, applying these ideas to problems in computational semantics has been difficult, at[…]

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Abstract As languages other than English are moving more and more into the focus of NLP, accurate handling of morphology is getting constantly more important. This talk presents approaches to morphological generation, casting morphological inflection[…]

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Abstract Deep Learning Acoustic Modeling has been widely deployed to real-world speech recognition products and services that benefit millions of users. In this talk, I will first briefly describe selected developments and investigations at Microsoft[…]

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