Sentence examples for Generate talking from inspiring English sources

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If ASCI White can generate talking points for IBM salespeople, it can do the same thing for the public relations crew at Lawrence Livermore and boy, could they use it.

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The generated talking heads using LIPS 2008 database were evaluated on the conference of Interspeech 2008.

AutoTutor has the option for one or two conversational agents (i.e., computer-generated talking heads) to promote verbal reasoning, question answering, conceptual understanding, and natural language interaction (Graesser 2016; Graesser et al. 2014; Nye et al. 2014a, 2014b).

Mr. Murakami's retrospective is expected to generate talk of a different sort.

(HY 3/2/11 14) When we describe that entire structure, e.g. as resembling a natural network of links (dàos) between temporally and spatially indexed points we can see how it might generate talk of a single cosmic dao.

How do the staff members seek to generate talk about medication-related problems?

In this article, we have argued that the staff members of a mental health TC in Italy rely on practices of topic initiation, which can be observed in mundane, non-institutional interactions, to generate talk about medication-related problems.

We hope to generate expressive talking heads by switching between the smile and the neutral mouth images.

Fifth, the Committee finds that the process used to generate the talking points HPSCI asked for--and which were used for Ambassador Rice's public appearances--was flawed.... Finally, the Committee found no evidence that any officer was intimidated, wrongly forced to sign a nondisclosure agreement or otherwise kept from speaking to Congress, or polygraphed because of their presence in Benghazi.

Our results show that DNN-based models outperform HMMs and unit selection on both the realism and expressiveness of the generated talking heads, while in terms of adaptation we can successfully adapt an audio-visual HMM set trained on a neutral speaking style database to a target emotion.

Towards this goal, we propose two deep neural network (DNN) architectures for Video-realistic Expressive Audio-Visual Text-To-Speech synthesis (EAVTTS) and evaluate them by comparing them directly both to traditional hidden Markov model (HMM) based EAVTTS, as well as a concatenative unit selection EAVTTS approach, both on the realism and the expressiveness of the generated talking head.

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