Atom decomposition based stress detection and automatic phrasing of speech

Tündik, Máté Ákos; Gerazov, Branislav; Gjoreski, Aleksandar; Szaszák, György

doi:10.1109/coginfocom.2016.7804519

Cited by 2 publications

(1 citation statement)

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“…Although textual information has been used in addition to acoustic features for the automatic focus detection [16], we are interested in working only with acoustic features because it simplifies the ACS and also makes it more language-independent. In this framework, [17] proposes a free-of-text automatic detection of stress on the Hungarian language at syllable level based on peaks of prosodic features.…”

Section: Related Workmentioning

confidence: 99%

Corrective Focus Detection in Italian Speech Using Neural Networks

2018

APH

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The corrective focus is a particular kind of prosodic prominence where the speaker is intended to correct or to emphasize a concept. This work develops an Artificial Cognitive System (ACS) based on Recurrent Neural Networks that analyzes suitable features of the audio channel in order to automatically identify the Corrective Focus on speech signals. Two different approaches to build the ACS have been developed. The first one addresses the detection of focused syllables within a given Intonational Unit whereas the second one identifies a whole IU as focused or not. The experimental evaluation over an Italian Corpus has shown the ability of the Artificial Cognitive System to identify the focus in the speaker IUs. This ability can lead to further important improvements in humanmachine communication. The addressed problem is a good example of synergies between Humans and Artificial Cognitive Systems.

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Section: Related Workmentioning

confidence: 99%