End-to-end and modular speech recognition systems differ in their architecture, training approaches, and practical implementation. End-to-end systems map audio inputs directly to text outputs using a single neural network, bypassing intermediate steps like phoneme or word alignment. Modular systems, in contrast, break the process into distinct components—such as acoustic modeling, pronunciation modeling, and language modeling—which are developed and optimized separately before being combined.
A key example of an end-to-end system is a model like DeepSpeech (Mozilla) or Wav2Vec (Meta), which uses a sequence-to-sequence architecture (e.g., transformers or RNNs) to learn audio-to-text mappings directly from data. These systems require large labeled datasets but avoid manual feature engineering. Modular systems, like those built with Kaldi, might use Gaussian Mixture Models (GMMs) or Hidden Markov Models (HMMs) for acoustic modeling, a pronunciation dictionary to map sounds to words, and a language model like n-grams or RNNs for context. For instance, Kaldi’s pipeline separates tasks like feature extraction (MFCCs), alignment, and decoding, allowing developers to swap components.
The main advantage of end-to-end systems is simplicity: they reduce engineering effort by eliminating handcrafted components and potential error propagation between modules. However, they often require more training data and compute resources. Modular systems offer flexibility: developers can debug or improve individual components (e.g., updating a language model for a new domain) without retraining the entire system. For example, adding medical jargon to a modular system’s language model is straightforward, whereas an end-to-end model would need retraining with domain-specific data. Modular systems also perform better in low-resource scenarios where labeled data is scarce, as components can be trained separately with smaller datasets.