# Modelling prosodic structure using Artificial Neural Networks

arXiv: https://arxiv.org/abs/1706.03952 · HTML: https://mlsanity.com/paper/1706.03952/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Assessing Language Disorders using Artificial Intelligence: a Paradigm Shift](https://mlsanity.com/paper/2305.20046/) (2023)

## Abstract (arXiv)

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.