Editing Development of End-to-End Models (section)

===== 1. Connectionist Temporal Classification (CTC) =====
As a technique to train an acoustic model without the need for precise frame-level alignments. Initially, using CTC to generate target phonemes didn't truly constitute an end-to-end method, as it still relied on language models. CTC allows for training an acoustic model without the necessity of frame-level alignments aligning acoustic data with the transcriptions.

The utilization of CTC as the loss function in training the acoustic model represents an end-to-end training approach. This method eliminates the necessity for prior data alignment, requiring only an input sequence and an output sequence for training. Consequently, manual alignment and labeling of data become unnecessary. Moreover, CTC's direct output sequence prediction doesn't require external post-processing. Within the CTC framework, 'blank' is introduced (denoting no predicted value for a given frame). Each prediction's classification corresponds to a spike in the speech waveform, while non-spike regions are considered 'blank.' In a speech sequence, CTC ultimately generates a spike sequence, irrespective of each phoneme's duration.