Search Results for author:
Found 16 papers, 4 papers with code
Open-Source Conversational AI with SpeechBrain 1.0
This paper presents SpeechBrain 1. 0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
End2End Acoustic to Semantic Transduction
In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism.
Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems
The dialog history is represented in the form of dialog history embedding vectors (so-called h-vectors) and is provided as an additional information to end-to-end SLU models in order to improve the system performance.
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
Multiple topic identification in telephone conversations
Furthermore, using the theme skeleton of a conversation from which thematic densities are derived, it will be possible to extract components of an automatic conversation report to be used for improving the service performance.
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
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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.
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.
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.
DECODA: a call-centre human-human spoken conversation corpus
The goal of the DECODA project is to reduce the development cost of Speech Analytics systems by reducing the need for manual annotat ion.
