Merging information in speech recognition: Feedback is never necessary

TLDR

Existing models such as TRACE and the modular Race model, which rely on feedback, fail to explain all phonemic decision‑making data. The authors aim to defend the thesis that feedback is unnecessary by analyzing lexical involvement in phonemic decision making. They introduce the Merge model, a modular framework in which information flows from prelexical processes to the lexicon without feedback. Merge accurately predicts lexical involvement in phonemic decisions, simulations demonstrate its ability to account for data through lexical competition, and the study concludes that feedback is unnecessary and modular models are best suited for speech recognition.

Abstract

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. The modular Race model (Cutler & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition.