2,858 research outputs found
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
Dialect Variation, Optionality, and the Learnability Guarantee
In acqumng a language the child is often faced with developing a grammar on the basis of input from a range of adults who speak different dialects or idiolects and whose grammars are not therefore identical. The fact that language acquisition is not subject to failure in such circumstances must mean that input from any combination of possible language varieties is guaranteed to trigger the development of a language system. The implications of this for the nature of Universal Grammar and the language acquisition process are explore
Exploring the effects of accent on cognitive processes: behavioral and electrophysiological insights
167 p.Previous research has found that speaker accent can have an impact on a range of offline and online cognitive processes (Baus, Bas, Calabria, & Costa, 2017; McAleer, Todorov, & Belin, 2014; Stevenage, Clarke, & McNeill, 2012; Sporer, 2001). Indeed, previous studies show that there are differences in native and non-native speech processing (Lev-Ari, 2018). Processing foreign-accented speech requires the listener to adapt to an extra range of variability, suggesting that there may be an increase in the amount of attentional and cognitive resources that are needed to successfully interpret the speech signal of a foreign-accented speaker. However, less is known about the differences between processing native and dialectal accents. Is dialectal processing more similar to foreign or native speech? To address this, two theories have been proposed (Clarke & Garrett, 2004; Floccia et al, 2009). Previous studies have contributed to the plausibility of both hypotheses and importantly for the purposes of this project, previous electroencephalography experiments exploring the question have mainly used sentences as material. More studies are needed to elucidate whether foreign accent is processed uniquely from all types of native speech (both native and dialectal accents) or whether dialectal accent is treated differently from native accent, despite both being native speech variations. Accordingly, the central aim of this dissertation is to further investigate processing mechanisms of speech accent across different levels of linguistic analysis using evidence from both behavioral and electrophysiological experiments. An additional aim of this project was to look at the effects of accent on information retention. In addition to fluctuations in attentional demands, it seems that non-native accent can lead to differences in the depth of listeners¿ memory encoding (Atkinson et al., 2005). This project further aimed to study how changing the accent of the information delivered may affect how well people remember the information received. Three experiments were carried out to investigate accent processing, results and future directions are discussed
Confidence-based Ensembles of End-to-End Speech Recognition Models
The number of end-to-end speech recognition models grows every year. These
models are often adapted to new domains or languages resulting in a
proliferation of expert systems that achieve great results on target data,
while generally showing inferior performance outside of their domain of
expertise. We explore combination of such experts via confidence-based
ensembles: ensembles of models where only the output of the most-confident
model is used. We assume that models' target data is not available except for a
small validation set. We demonstrate effectiveness of our approach with two
applications. First, we show that a confidence-based ensemble of 5 monolingual
models outperforms a system where model selection is performed via a dedicated
language identification block. Second, we demonstrate that it is possible to
combine base and adapted models to achieve strong results on both original and
target data. We validate all our results on multiple datasets and model
architectures.Comment: To appear in Proc. INTERSPEECH 2023, August 20-24, 2023, Dublin,
Irelan
- …