Prosodic and pragmatic values of discourse particles in French

Lee, Lou; Bartkova, Katarina; Dargnat, Mathilde; Jouvet, Denis

doi:10.36505/exling-2018/09/0018/000351

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“…The pragmatic function typically indicates the role of the discourse particle in structuring the speech flow, as for example: introduction, conclusion, interruption, etc. Prosodic correlates have been analyzed with respect to the pragmatic functions of the discourse particles [33] As different discourse particles sharing a same pragmatic function often exhibits a set of similar prosodic patterns, experiments have been conducted to investigate their interchangeability [34] when using only textual information, or when using audio plus textual information.…”

Section: Prosodic Correlates Of Discourse Particlesmentioning

confidence: 99%

Speech Processing and Prosody

Jouvet

2019

Text, Speech, and Dialogue

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The prosody of the speech signal conveys information over the linguistic content of the message: prosody structures the utterance, and also brings information on speaker's attitude and speaker's emotion. Duration of sounds, energy and fundamental frequency are the prosodic features. However their automatic computation and usage are not obvious. Sound duration features are usually extracted from speech recognition results or from a force speech-text alignment. Although the resulting segmentation is usually acceptable on clean native speech data, performance degrades on noisy or not non-native speech. Many algorithms have been developed for computing the fundamental frequency, they lead to rather good performance on clean speech, but again, performance degrades in noisy conditions. However, in some applications, as for example in computer assisted language learning, the relevance of the prosodic features is critical; indeed, the quality of the diagnostic on the learner's pronunciation will heavily depend on the precision and reliability of the estimated prosodic parameters. The paper considers the computation of prosodic features, shows the limitations of automatic approaches, and discusses the problem of computing confidence measures on such features. Then the paper discusses the role of prosodic features and how they can be handled for automatic processing in some tasks such as the detection of discourse particles, the characterization of emotions, the classification of sentence modalities, as well as in computer assisted language learning and in expressive speech synthesis.

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Section: Prosodic Correlates Of Discourse Particlesmentioning

confidence: 99%