  In speech recognition problems, data scarcity often poses an issue due to the
willingness of humans to provide large amounts of data for learning and
classification. In this work, we take a set of 5 spoken Harvard sentences from
7 subjects and consider their MFCC attributes. Using character level LSTMs
(supervised learning) and OpenAI's attention-based GPT-2 models, synthetic
MFCCs are generated by learning from the data provided on a per-subject basis.
A neural network is trained to classify the data against a large dataset of
Flickr8k speakers and is then compared to a transfer learning network
performing the same task but with an initial weight distribution dictated by
learning from the synthetic data generated by the two models. The best result
for all of the 7 subjects were networks that had been exposed to synthetic
data, the model pre-trained with LSTM-produced data achieved the best result 3
times and the GPT-2 equivalent 5 times (since one subject had their best result
from both models at a draw). Through these results, we argue that speaker
classification can be improved by utilising a small amount of user data but
with exposure to synthetically-generated MFCCs which then allow the networks to
achieve near maximum classification scores.
