Search Results for author:
Found 9 papers, 1 papers with code
End-to-End real time tracking of children's reading with pointer network
To train this pointer network, we generate ground truth training signals by using forced alignment between the read speech and the text being read on the training set.
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations.
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data
As it is difficult to collect spoken data from users without a functioning SLU system, our method does not rely on spoken data for training, rather we use an ASR error predictor to "speechify" the text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Hallucination of speech recognition errors with sequence to sequence learning
Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation
Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels.
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis
Our interest in this paper is in meeting a rapidly growing industrial demand for information extraction from images of documents such as invoices, bills, receipts etc.
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning
We further train a meta agent with a mixture of behaviors by learning an ensemble of different models using reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles
We present an approach where two different models (Deep and Shallow) are trained separately on the data and a weighted average of the outputs is taken as the final result.
