CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks
Table 1: Five lexical items use for training in the five-word ciwGAN model with their corresponding IPA transcription (based on general American English) and counts of data points for each item.
['word', 'IPA', 'data points']
The first model is trained on the ciwGAN architecture with 5 lexical items from TIMIT: oily, rag, suit, water, and year. The latent space of this network includes 5 categorical variables (c) constituting a five-level one-hot vector and 95 random variables z. The five lexical items were chosen based on frequency: they are chosen from the most frequent content words with at least 600 data points in TIMIT. A total of 3205 data points were used in training and each of the five items has >600 data points in the training data. The input data are 16-bit .wav slices of lexical items (as annotated in TIMIT) sampled with 16 kHz rate.