Automatic Speech Recognition

Automatic speech recognition converts an acoustic signal into words, subwords or characters. The problem combines acoustic representation, temporal alignment, language information and search.

Classical architectures

Traditional systems combine MFCC-like features, acoustic models, HMM states, pronunciation lexicons and n-gram language models. WFST decoding can integrate these components into a single search graph.

End-to-end models

In automatic speech recognition, CTC reduces the need for frame-level alignment. RNN-T is well suited to streaming recognition. Transformer and Conformer architectures model long-range context effectively.

End-to-end modelling does not remove the need for VAD, resampling, channel handling and segmentation. These stages determine the structure of the model input.

Domain adaptation

Technical terms, names and organisation-specific vocabulary may be weakly represented by general language models. Contextual biasing, rescoring and domain adaptation can improve recognition of such terms.

Word Error Rate is the standard aggregate metric. Numbers, names and critical terms can also be evaluated separately.

For real-time systems, WER should be considered together with real-time factor, segment latency, timestamp accuracy and result stability.