Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
📰 ArXiv cs.AI
Learn how unsupervised deep neural networks can model basic syntax from raw speech using spontaneous concatenation, a crucial step in syntax evolution
Action Steps
- Train a ciwGAN or fiwGAN model on raw speech data using convolutional neural networks
- Apply spontaneous concatenation to model basic syntax from speech
- Evaluate the performance of the model on syntax evolution tasks
- Compare the results with traditional text-based syntax models
- Fine-tune the model to improve its ability to learn syntax from speech
Who Needs to Know This
NLP researchers and AI engineers can benefit from this study to improve language models and speech processing systems
Key Insight
💡 Spontaneous concatenation is a powerful tool for modeling syntax evolution from speech, enabling fully unsupervised learning
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🗣️ Unsupervised deep neural networks can learn basic syntax from raw speech using spontaneous concatenation! #NLP #AI
Key Takeaways
Learn how unsupervised deep neural networks can model basic syntax from raw speech using spontaneous concatenation, a crucial step in syntax evolution
Full Article
Title: Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
Abstract:
arXiv:2305.01626v4 Announce Type: replace-cross Abstract: Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary suboperations of syntax -- concatenation. We introduce \textit{spontaneous concatenation}: a phenomenon where a ciwGAN/fiwGAN models (based on convolutional neural networks) trained on
Abstract:
arXiv:2305.01626v4 Announce Type: replace-cross Abstract: Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary suboperations of syntax -- concatenation. We introduce \textit{spontaneous concatenation}: a phenomenon where a ciwGAN/fiwGAN models (based on convolutional neural networks) trained on
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