Search Results for author:
Found 13 papers, 5 papers with code
Remembering Winter Was Coming: Character-Oriented Video Summaries of TV Series
In this paper, we tackle plot modeling by considering the social network of interactions between the characters involved in the narrative: substantial, durable changes in a major character's social environment suggest a new development relevant for the summary.
Real to H-space Encoder for Speech Recognition
Deep neural networks (DNNs) and more precisely recurrent neural networks (RNNs) are at the core of modern automatic speech recognition systems, due to their efficiency to process input sequences.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features
In recent years, online social networks have allowed worldwide users to meet and discuss.
M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding
TRS transcripts are only used to measure the performances of ASR systems.
Generative Adversarial Network
Spoken Language Understanding
Audiovisual speaker diarization of TV series
Speaker diarization may be difficult to achieve when applied to narrative films, where speakers usually talk in adverse acoustic conditions: background music, sound effects, wide variations in intonation may hide the inter-speaker variability and make audio-based speaker diarization approaches error prone.
Speech recognition with quaternion neural networks
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition
Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models.
Ranked #19 on
Speech Recognition
on TIMIT
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Quaternion Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
One of the main difficulty in using topic model on huge data collection is related to the material resources (CPU time and memory) required for model estimate.
Parallel Long Short-Term Memory for Multi-stream Classification
Nevertheless, these RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM) directions.
Narrative Smoothing: Dynamic Conversational Network for the Analysis of TV Series Plots
In order to assess our method, we apply it to a new corpus of 3 popular TV series, and compare it to both standard approaches.
Learning to retrieve out-of-vocabulary words in speech recognition
In this paper, we propose two neural network models targeted to retrieve OOV PNs relevant to an audio document: (a) Document level Continuous Bag of Words (D-CBOW), (b) Document level Continuous Bag of Weighted Words (D-CBOW2).
