# Effects of Word Embeddings on Neural Network-based Pitch Accent Detection

arXiv: https://arxiv.org/abs/1805.05237 · HTML: https://mlsanity.com/paper/1805.05237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 2 of 2)

- [Automatic recognition of suprasegmentals in speech](https://mlsanity.com/paper/2108.01122/) (2021)
- [Deep Learning For Prominence Detection In Children's Read Speech](https://mlsanity.com/paper/2110.14273/) (2021)

## Abstract (arXiv)

Pitch accent detection often makes use of both acoustic and lexical features based on the fact that pitch accents tend to correlate with certain words. In this paper, we extend a pitch accent detector that involves a convolutional neural network to include word embeddings, which are state-of-the-art vector representations of words. We examine the effect these features have on within-corpus and cross-corpus experiments on three English datasets. The results show that while word embeddings can improve the performance in corpus-dependent experiments, they also have the potential to make generalization to unseen data more challenging.