scholarly journals Prosodic Phrase Alignment for Machine Dubbing

Author(s):  
Alp Öktem ◽  
Mireia Farrús ◽  
Antonio Bonafonte
2010 ◽  
pp. 84-102
Author(s):  
P. Toyoko Kang

This chapter provides an argument endorsing blendedlearning and teaching for foreign language (FL)/second language (L2) courses, in lieu of total online learning andteaching or total face-to-face learning and teaching (FFLT). Two main arguments are posed, citing concrete examples. First, that in total online learning and teaching, one of the greatest challenges is to reduce the psychological and social distance between teacher and student that leads to a dysfunctional parser (a mental language processor) for FL/L2. And secondly, online learning and teachingencourage more input, hence clarify communication---by making not only currently incomprehensible input comprehensible but also hard-tobe-comprehended output easy-to-comprehend---- through “self-negotiation of form and meaning,” and the parser’s strategy of being “first (prosodic phrase) come, first interpreted/processed.” This chapter proceeds to strongly recommend that FL/L2 teachers make simple audio files to provide their students with spoken input to prevent students from employing the L1 strategy of “first come, last interpreted/ processed.” Furthermore, this chapter shows what kind of spoken input is to be recorded in audio files for students in Elementary Japanese II and Intermediate Japanese I.


2012 ◽  
Vol 20 (2) ◽  
pp. 512-523
Author(s):  
Graham Neubig ◽  
Taro Watanabe ◽  
Eiichiro Sumita ◽  
Shinsuke Mori ◽  
Tatsuya Kawahara

2019 ◽  
Vol 9 (16) ◽  
pp. 3295 ◽  
Author(s):  
Victoria Mingote ◽  
Antonio Miguel ◽  
Alfonso Ortega ◽  
Eduardo Lleida

In this paper, we propose a new differentiable neural network with an alignment mechanism for text-dependent speaker verification. Unlike previous works, we do not extract the embedding of an utterance from the global average pooling of the temporal dimension. Our system replaces this reduction mechanism by a phonetic phrase alignment model to keep the temporal structure of each phrase since the phonetic information is relevant in the verification task. Moreover, we can apply a convolutional neural network as front-end, and, thanks to the alignment process being differentiable, we can train the network to produce a supervector for each utterance that will be discriminative to the speaker and the phrase simultaneously. This choice has the advantage that the supervector encodes the phrase and speaker information providing good performance in text-dependent speaker verification tasks. The verification process is performed using a basic similarity metric. The new model using alignment to produce supervectors was evaluated on the RSR2015-Part I database, providing competitive results compared to similar size networks that make use of the global average pooling to extract embeddings. Furthermore, we also evaluated this proposal on the RSR2015-Part II. To our knowledge, this system achieves the best published results obtained on this second part.


2015 ◽  
Vol 38 (2) ◽  
pp. 115-147 ◽  
Author(s):  
Sara Myrberg ◽  
Tomas Riad

We give an overview of the phonological properties and processes that define the categories of the prosodic hierarchy in Swedish: the prosodic word (ω), the prosodic phrase (φ) and the intonation phrase (ι). The separation of two types of tonal prominence, big accents versus small accents (previously called focal and word accent, e.g. Bruce 1977, 2007), is crucial for our analysis. The ω in Swedish needs to be structured on two levels, which we refer to as the minimal ω and the maximal ω, respectively. The minimal ω contains one stress, whereas the maximal ω contains one accent. We argue for a separate category φ that governs the distribution of big accents within clauses. The ι governs the distribution of clause-related edge phenomena like the initiality accent and right-edge boundary tones as well as the distribution of nuclear big accents.


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