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Found 10 papers, 1 papers with code
Using i-vectors for subject-independent cross-session EEG transfer learning
Cognitive load classification is the task of automatically determining an individual's utilization of working memory resources during performance of a task based on physiologic measures such as electroencephalography (EEG).
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision
Advances in self-supervised learning have significantly reduced the amount of transcribed audio required for training.
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition
Final performance is an additional 2% better absolute when using CTC-based decoding for semi-supervised training compared to shallow fusion.
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts
We show that there is a sizable initial gap in such a data condition between hybrid and seq2seq models, and the hybrid model is able to further improve through the use of additional language model (LM) data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition
Labeled code-switched data are rare, so monolingual data are often used to model code-switched speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
The 2019 BBN Cross-lingual Information Retrieval System
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
Reformulating Information Retrieval from Speech and Text as a Detection Problem
In the IARPA MATERIAL program, information retrieval (IR) is treated as a hard detection problem; the system has to output a single global ranking over all queries, and apply a hard threshold on this global list to come up with all the hypothesized relevant documents.
Learning from Noisy Labels with Noise Modeling Network
Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training data.
Cross-lingual Information Retrieval with BERT
Multiple neural language models have been developed recently, e. g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking.
Towards a New Understanding of the Training of Neural Networks with Mislabeled Training Data
This includes results about the ability of the noisy model to make the same decisions as the clean model and the effects of noise on model performance.
