Prediction and error in early infant speech learning: A speech acquisition model
| Autor: | Jessie S. Nixon, Fabian Tomaschek |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Consonant
Delta rule Linguistics and Language Speech acquisition Speech perception Cognitive Neuroscience Speech recognition Experimental and Cognitive Psychology Language Development Article 050105 experimental psychology Language and Linguistics 03 medical and health sciences 0302 clinical medicine Discriminative model Error-driven learning Phonetics Vowel Developmental and Educational Psychology Humans Learning Speech 0501 psychology and cognitive sciences Child Cluster analysis 05 social sciences Infant Statistical learning Sound recording and reproduction Discriminative learning A priori and a posteriori Psychology 030217 neurology & neurosurgery Rescorla-Wagner equations |
| Zdroj: | Cognition |
| Popis: | In the last two decades, statistical clustering models have emerged as a dominant model of how infants learn the sounds of their language. However, recent empirical and computational evidence suggests that purely statistical clustering methods may not be sufficient to explain speech sound acquisition. To model early development of speech perception, the present study used a two-layer network trained with Rescorla-Wagner learning equations, an implementation of discriminative, error-driven learning. The model contained no a priori linguistic units, such as phonemes or phonetic features. Instead, expectations about the upcoming acoustic speech signal were learned from the surrounding speech signal, with spectral components extracted from an audio recording of child- directed speech as both inputs and outputs of the model. To evaluate model performance, we simulated infant responses in the high-amplitude sucking paradigm using vowel and fricative pairs and continua. The simulations were able to discriminate vowel and consonant pairs and predicted the infant speech perception data. The model also showed the greatest amount of discrimination in the expected spectral frequencies. These results suggest that discriminative error-driven learning may provide a viable approach to modelling early infant speech sound acquisition. |
| Databáze: | OpenAIRE |
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