What are the advantages of deep learning–based speech recognition algorithms?

Deep learning–based speech recognition offers two key advantages over traditional approaches:

Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.

End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.

Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.

Exact Extract from HCIP-AI EI Developer V2.5:

"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs."