Transducermodellen

Transducermodellen, or the transducer model, is a term used in computational linguistics and cognitive science to describe a class of models that formalize the transformation (transduction) of input representations into output representations. The central idea is to model how an input sequence maps to an output sequence, possibly via intermediate representations or alignments.

In natural language processing and speech technology, transducer models come in two main families: traditional finite-state transducers (FSTs) and neural sequence transducers. FSTs implement deterministic or nondeterministic state machines that perform regular string-to-string mappings, and they are widely used for tasks such as morphological analysis, phonology, text normalization, and grapheme-to-phoneme conversion. They are valued for interpretability, compositionality, and efficiency in well-defined rule-based pipelines.

Neural transducer models extend the concept to learned mappings from input sequences to output sequences. They include architectures such as the Recurrent Neural Network Transducer (RNN-T) and Transformer-based transducers. These models typically consist of an encoder that processes the input sequence, a predictor or decoder that models the output sequence, and a joining mechanism that combines the two to emit output symbols, often allowing for insertions and alignment between input and output. They are trained with a transducer loss that accounts for all valid input-output alignments and are commonly used for streaming automatic speech recognition, handwriting recognition, and end-to-end translation.

Applications of transducer models emphasize robust sequence alignment, streaming processing, and end-to-end learning. Limitations include data and computational requirements, challenges with long-range dependencies, and the need for careful design to ensure reliable alignment and interpretability. The term is sometimes used interchangeably with “sequence transduction model,” depending on context.