Attention-LSTM autoencoder simulation for phonotactic learning from raw audio input

Abstract This paper presents a learning simulation of phonotactics using an attention-based long short-term memory autoencoder trained on raw audio input. Unlike previous models that use abstract phonological representations, the current method imitates early phonotactic acquisition stages by processing continuous acoustic signals. Focusing on an English phonotactic pattern, specifically the distribution of aspirated and unaspirated voiceless stops, the model implicitly acquires phonotactic knowledge through reconstruction tasks. The results demonstrate the model’s ability to acquire essential phonotactic relations through attention mechanisms, exhibiting increased attention to phonological context which shows higher phonotactic predictability. The learning trajectory begins with a strong reliance on contextual cues to identify phonotactic patterns. Over time, the system internalizes these constraints, leading to a decreased reliance on specific phonotactic cues. This study suggests the feasibility of early phonotactic learning models based on raw auditory input and provides insights into both computational modeling and infants’ phonotactic acquisition.