Concept
N-gramme
Publications associées (10)
Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain
Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...
EPFL2024Comparison of Subword Segmentation Methods for Open-vocabulary ASR using a Difficulty Metric
Philip Neil Garner, Claudiu-Cristian Musat
We experiment with subword segmentation approaches that are widely used to address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR). For morphologically rich languages such as German which has many rare words main ...
2020COMPARISON OF SUBWORD SEGMENTATION METHODS FOR OPEN-VOCABULARYEND-TO-END SPEECH RECOGNITION
Philip Neil Garner, Claudiu-Cristian Musat
To address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR), we experiment with subword segmentation approaches, specifically byte-pair encoding and unigram language model. Such approaches are attractive in genera ...
Idiap2020N-gram-Based Low-Dimensional Representation for Document Classification
Rémi Philippe Lebret, Ronan Collobert
The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent D ...
2015Language modeling based on neural clustering of words
This document describes a neural method for clustering words and its use in language modeling for speech recognizers. The method is based on clustering the words which appear on similar local context and estimating the parameters needed for language modeli ...
IDIAP2000