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Modelling prosodic structure using Artificial Neural Networks

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Jean-Philippe Bernardy, Charalambos Themistocleous

Computation and Language · Submitted 13 Jun 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy.

how it is stated · asserted 1.00 read the record →

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arXiv ID1706.03952 (version 2)
Pith ID4TSTZ25D, Pith's permanent record of this paper and its review
CitationCharalambos Themistocleous, Jean-Philippe Bernardy. "Modelling prosodic structure using Artificial Neural Networks." arXiv:1706.03952 (2017). https://pith.science/paper/4TSTZ25D
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,202 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.11 · human participants 0.21 · controlled comparison 0.23 · randomized assignment 0.02 · states quantitative result 0.98 · compares with prior results 0.87 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. This paper provides a classification model of Cypriot Greek questions and statements. We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. background 1.00 0.04 0.02 asserted 1.00
s1 However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. background 0.87 0.05 0.02 asserted 1.00
s2 This paper provides a classification model of Cypriot Greek questions and statements. contribution 1.00 0.82 0.05 asserted 0.97
s3 We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). method 0.98 0.80 0.04 asserted 0.82
s4 The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy. result 1.00 0.94 0.82 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.56 · evidence from observation or experiment 0.72.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. This paper provides a classification model of Cypriot Greek questions and statements. We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy.

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