Extending AuToBI to prominence detection in European Portuguese

Moniz, Helena; Mata, Ana Isabel; Hirschberg, Julia; Batista, Fernando; Rosenberg, Andrew; Trancoso, Isabel

doi:10.21437/speechprosody.2014-44

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…Overall, the supervised performance of the best feature combination is close to the result of ACC = 89.03% reported in the literature using Bidirectional Recurrent Neural networks [26], although, as mentioned, experiments with different corpora are not directly comparable. The performance is also higher than many other recent approaches using a variety of features and classifiers [18,38,39,40,41].…”

Section: Resultsmentioning

confidence: 81%

Analyzing the Contribution of Top-Down Lexical and Bottom-Up Acoustic Cues in the Detection of Sentence Prominence

Kakouros¹,

Pelemans²,

Verwimp³

et al. 2016

Interspeech 2016

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Recent work has suggested that prominence perception could be driven by the predictability of the acoustic prosodic features of speech. On the other hand, lexical predictability and part of speech information are also known to correlate with prominence. In this paper, we investigate how the bottom-up acoustic and top-down lexical cues contribute to sentence prominence by using both types of features in unsupervised and supervised systems for automatic prominence detection. The study is conducted using a corpus of Dutch continuous speech with manually annotated prominence labels. Our results show that unpredictability of speech patterns is a consistent and important cue for prominence at both the lexical and acoustic levels, and also that lexical predictability and part-of-speech information can be used as efficient features in supervised prominence classifiers.

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Section: Resultsmentioning

confidence: 81%

Analyzing the Contribution of Top-Down Lexical and Bottom-Up Acoustic Cues in the Detection of Sentence Prominence

Kakouros¹,

Pelemans²,

Verwimp³

et al. 2016

Interspeech 2016

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“…If we consider Neural Networks methodologies in this area, the number of researches decrease considerably, and it is really limited narrowing down to the Italian language [18,19]. Multiple types of stresses have been studied and classified with standard Feedforward Neural Networks [20,21,22] and with Convolutional Neural Networks [23,24] with more success than other machine learning techniques.…”

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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“…System Architecture used in the study is based on research conducted by Fabio Tamburini et al for Prosodic Prominence Detection (Tamburini et al, 2014). The illustration is shown in Fig.…”

Section: B System Architecturementioning

confidence: 99%

Fuzzy Latent-Dynamic Conditional Neural Fields for Gesture Recognition in Video

Yulita

Fanany

Arymurthy

2017

ijoict

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With the explosion of data on the internet led to the presence of the big data era, so it requires data processing to get the useful information. One of the challenges is the gesture recognition the video processing. Therefore, the study proposes Latent-Dynamic Conditional Neural Fields and compares with the other family members of Conditional Random Fields. For improving the accuracy, these methods are combined by using Fuzzy Clustering. From the results, it can be concluded that the performance of Fuzzy Latent-Dynamic Conditional Neural Fields are the highest. Also, the combination of the basic classifiers and Fuzzy C-Means Clustering has the higher than the original ones. The evaluation is tested on a temporal dataset of gesture phase segmentation.

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Extending AuToBI to prominence detection in European Portuguese

Cited by 12 publications

References 22 publications

Analyzing the Contribution of Top-Down Lexical and Bottom-Up Acoustic Cues in the Detection of Sentence Prominence

Analyzing the Contribution of Top-Down Lexical and Bottom-Up Acoustic Cues in the Detection of Sentence Prominence

Corrective Focus Detection in Italian Speech Using Neural Networks

Fuzzy Latent-Dynamic Conditional Neural Fields for Gesture Recognition in Video

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