End-to-end Whispered Speech Recognitionwith Frequency-weighted Approaches and Layer-wise Transfer Learning
Table 1: PERs(%) on whispered for hybrid and E2E models trained on wTIMIT whispered speech with different corpus partitions.
['[BOLD] Corpus Partition', '[BOLD] ASR Model', '[BOLD] Whispered  [BOLD] Dev', '[BOLD] Whispered  [BOLD] Test']
The following two datasets were used in our experiments: wTIMIT. The whispered TIMIT corpus [Lim10-wtimit] consisted of parallel whispered and normal speech data each around 26 hours, including 48 speakers whispering and speaking 450 phonetically balanced sentences chosen from TIMIT [Garofolo93-timit]. It was originally partitioned into the TRAIN and TEST set randomly. We thus re-partitioned the dataset into train/dev/test sets, each containing 400/25/25 sentences split from the 450 sentences. We first compared the HMM-based hybrid model with standard E2E ASR without any approach proposed here, assuming only the whispered part of wTIMIT was available for training. The phoneme level (total 39 phonemes) The light E2E ASR model used to produce Fig. So all experiments below were based on our partition as mentioned in Sec. Here we see the E2E model was slightly better than the hybrid (rows(d) v.s. (c)), even with only 26 hours of training data, for which hybrid typically outperformed E2E. This indicated E2E ASR was a more proper choice for whispered speech if the data set was not too small.