Unsupervised Word Segmentation from Speech with Attention
Table 3: AUD methods differ in their ability to encode speech utterances (left side); which impacts the final segmentation of the attentional model (right side).
['[EMPTY]', '[BOLD] Phones per Sentence avg', '[BOLD] Phones per Sentence max', '[BOLD] Phones per Sentence min', '[BOLD] Tokens per Sentence avg', '[BOLD] Tokens per Sentence max', '[BOLD] Tokens per Sentence min']
Another additional explanation might be that shorter sequences of symbols are easier to segment. For instance, even if the attention helps the system to better deal with long sentences, it is still prone to performance degradation when faced with very long sequences of symbols We observe that the HMM model uses more symbols to represent an utterance, while the SVAE model offers a more concise representation. This best model also achieved a vocabulary size close to the topline, of 14,837 types compared to 13,878. This is another clue as to why the MBN SVAE is performing best on our task.