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Found 20 papers, 4 papers with code
Quaternion Neural Networks for Multi-channel Distant Speech Recognition
In this paper, we propose to capture these inter- and intra- structural dependencies with quaternion neural networks, which can jointly process multiple signals as whole quaternion entities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
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
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.
Systèmes du LIA à DEFT'13
The 2013 D\'efi de Fouille de Textes (DEFT) campaign is interested in two types of language analysis tasks, the document classification and the information extraction in the specialized domain of cuisine recipes.
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.
Un Corpus de Flux TV Annot\'es pour la Pr\'ediction de Genres (A Genre Annotated Corpus of French Multi-channel TV Streams for Genre Prediction)
Cet article pr{\'e}sente une m{\'e}thode de pr{\'e}diction de genres d{'}{\'e}missions t{\'e}l{\'e}vis{\'e}es couvrant 2 jours de diffusion de 4 cha{\^\i}nes TV fran{\c{c}}aises structur{\'e}s en {\'e}missions annot{\'e}es en genres.
Auto-encodeurs pour la compr\'ehension de documents parl\'es (Auto-encoders for Spoken Document Understanding)
Les repr{\'e}sentations de documents au moyen d{'}approches {\`a} base de r{\'e}seaux de neurones ont montr{\'e} des am{\'e}liorations significatives dans de nombreuses t{\^a}ches du traitement du langage naturel.
Un Sous-espace Th\'ematique Latent pour la Compr\'ehension du Langage Parl\'e (A Latent Topic-based Subspace for Spoken Language Understanding)
Les applications de compr{\'e}hension du langage parl{\'e} sont moins performantes si les documents transcrits automatiquement contiennent un taux d{'}erreur-mot {\'e}lev{\'e}.
Utilisation d'annotations s\'emantiques pour la validation automatique d'hypoth\`eses dans des conversations t\'el\'ephoniques
Les travaux pr{\'e}sent{\'e}s portent sur l{'}extraction automatique d{'}unit{\'e}s s{\'e}mantiques et l{'}{\'e}valuation de leur pertinence pour des conversations t{\'e}l{\'e}phoniques.
Apport de l'information temporelle des contextes pour la repr\'esentation vectorielle continue des mots
Ces approches sont manipul{\'e}es au travers d{'}un r{\'e}seau de neurones, l{'}architecture CBOW cherchant alors {\`a} pr{\'e}dire un mot sachant son contexte, alors que l{'}architecture Skip-Gram pr{\'e}dit un contexte sachant un mot.
Initialisation de R\'eseaux de Neurones \`a l'aide d'un Espace Th\'ematique
La m{\'e}thode propos{\'e}e consiste {\`a} configurer la topologie d{'}un ANN ainsi que d{'}initialiser les connexions de celui-ci {\`a} l{'}aide des espaces th{\'e}matiques appris pr{\'e}c{\'e}demment.
Characterizing and Predicting Bursty Events: The Buzz Case Study on Twitter
The prediction of bursty events on the Internet is a challenging task.
A LDA-Based Topic Classification Approach From Highly Imperfect Automatic Transcriptions
Although the current transcription systems could achieve high recognition performance, they still have a lot of difficulties to transcribe speech in very noisy environments.
