Discovering consistent word confusions in noise
Listeners make mistakes when communicating under adverse conditions, with overall error rates reasonably well-predicted by existing speech intelligibility metrics. However, a detailed examination of confusions made by a majority of listeners is more likely to provide insights into processes of normal word recognition. The current study measured the rate at which robust misperceptions occurred for highly-confusable words embedded in noise. In a second experiment, confusions discovered in the first listening test were subjected to a range of manipulations designed to help identify their cause. These experiments reveal that while majority confusions are quite rare, they occur sufficiently often to make large-scale discovery worthwhile. Surprisingly few misperceptions were due solely to energetic masking by the noise, suggesting that speech and noise “react” in complex ways which are not well-described by traditional masking concepts.
Abstract : Our group has been working in the following directions: 1. Testing and tuning our time series classification algorithm, 2. Perfectioning the use of entropy in language models as a tool to reconstruct the context of a given meme/conversation, 3. formalizing in an operational sense the definition of campaign and its associated features, 4. collecting relevant datasets to test detection algorithms.