The neural generation and control of speech utterances is a complex process that is still not fully understood. However, several neurobiologically inspired models have been proposed that describe the hierarchical control concept of utterance generation (e.g., Hickok and Poeppel (2012); Bohland et al. (2010); Kröger et al. (2020); Parrell et al. (2018)). This process begins with the neural activation of the cognitive-linguistic representation of an utterance, followed by a higher-level premotor representation, leading to neuromuscular activation patterns, and finally to the articulatory-acoustic realization of the utterance (cf.

Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Tennis play at the US Open consists of 254 matches in the men's and women's singles events totaling tens of thousands of points. During the tournament's two weeks, many matches are played in parallel, and it's virtually impossible for any tennis fan, or the editorial team at the United States Tennis Association (USTA), to capture any sizable percentage of the best points. To help solve this challenge, IBM built an AI system that clips and creates candidate highlight videos and assigns a fair excitement score, all within two minutes of the end of each match. Every highlight is ranked so that tennis fans and video editors at the USTA and its broadcast partners can see the most exciting points of the tournament, while minimizing the influence of player gestures, match analytic score, player rank, player age and crowd size.