Abstract
This paper proposes a framework for modeling sound change that combines deep convolutional neural networks and iterative learning. Acquisition and transmission of speech across generations is modeled by training generations of Generative Adversarial Networks (Goodfellow et al. arXiv:1406.2661,Donahue et al. arXiv:1705.07904) on unannotated raw speech data. The paper argues that several properties of sound change emerge from the proposed architecture. Four generations of Generative Adversarial Networks were trained on an allophonic distribution in English where voiceless stops are aspirated word-initially before stressed vowels except if preceded by [s]. The first generation of networks is trained on the relevant sequences in human speech from the TIMIT database. The subsequent generations are not trained on TIMIT, but on generated outputs from the previous generation and thus start learning from each other in an iterative learning task. The initial allophonic distribution is progressively being lost with each generation, likely due to pressures from the global distribution of aspiration in the training data that resembles phonological pressures in natural language. The networks show signs of a gradual shift in phonetic targets characteristic of a gradual phonetic sound change. At endpoints, the networks' outputs superficially resemble a phonological change -- rule loss -- driven by imperfect learning. The model features signs of stability, one of the more challenging aspects of computational models of sound change. The results suggest that the proposed Generative Adversarial models of phonetic and phonological acquisition have the potential to yield new insights into the long-standing question of how to model language change.
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URL
https://arxiv.org/abs/2011.05463
