SLAM: Automatic Stylization and Labelling of Speech Melody

By Nicolas Obin, Julie Beliao, Christophe Veaux, Anne Lacheret. 2014

51 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on Neural Speech Synthesis (2021, methodology): “For example, the prosody information can be labeled according to some annotation schemas, such as ToBI [301], AuToBI [288], Tilt [339], INTSINT [109], and SLAM [243].”
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron (2018, methodology): “Numerous works have explored annotation schemes for diagramming and automatic labeling of prosody: ToBi (Silverman et al., 1992), AuToBI (Rosenberg, 2010), Tilt (Taylor, 1998), INTSINT (Hirst, 2001), SLAM (Obin et al., 2014) all describe methods for the annotation and automatic extraction of labels ”
Predicting Expressive Speaking Style from Text in End-To-End Speech Synthesis (2018, methodology): “INTSINT [3], ToBi [4], Momel [5], landmark detection [6], Tilt [7], and SLAM [8] all describe methods to annotate or classify prosodic features such as breaks, intonation, rhythm, and melody.”
A Template-Based Approach for Speech Synthesis Intonation Generation Using LSTMs (2016, unknown): “While the vast majority of F0 prediction approaches are based on regression rather than classiﬁcation, the idea of clustering and/or discretising F0 contours is not new (cf. [12, 13]).”