We are pleased to announce the publication of a new paper by Frank Lihui Tan and Youngah Do in the journal Linguistics Vanguard. The paper, titled “Attention-LSTM autoencoder simulation for phonotactic learning from raw audio input,” explores a novel approach to phonotactic learning using an attention-based long short-term memory (LSTM) autoencoder trained on raw audio input.
Unlike previous models that rely on abstract phonological representations, this study simulates early phonotactic acquisition stages by processing continuous acoustic signals. The research focuses on an English phonotactic pattern, specifically the distribution of aspirated and unaspirated voiceless stops. The model implicitly acquires phonotactic knowledge through reconstruction tasks, demonstrating its ability to capture essential phonotactic relations via attention mechanisms. The findings suggest that the model initially relies heavily on contextual cues to identify phonotactic patterns but gradually internalizes these constraints, reducing its dependence on specific phonotactic cues over time.
This study provides valuable insights into both computational modeling and infants’ phonotactic acquisition, highlighting the feasibility of early phonotactic learning models based on raw auditory input.
Tan, F. & Do, Y. (2025). Attention-LSTM autoencoder simulation for phonotactic learning from raw audio input. Linguistics Vanguard. open_in_newDOI