Results 141 to 148 of about 47,965 (148)

Triggered Attention for End-to-end Speech Recognition

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
A new system architecture for end-to-end automatic speech recognition (ASR) is proposed that combines the alignment capabilities of the connectionist temporal classification (CTC) approach and the modeling strength of the attention mechanism. The proposed system architecture, named triggered attention (TA), uses a CTC-based classifier to control the ...
Niko Moritz, Takaaki Hori, Jonathan Le Roux   +2 more
openaire   +1 more source

End-to-End Speech Recognition Models

2016
For the past few decades, the bane of Automatic Speech Recognition (ASR) systems have been phonemes and Hidden Markov Models (HMMs). HMMs assume conditional indepen-dence between observations, and the reliance on explicit phonetic representations requires expensive handcrafted pronunciation dictionaries.
openaire   +1 more source

End-to-End Multi-Speaker Speech Recognition

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Current advances in deep learning have resulted in a convergence of methods across a wide range of tasks, opening the door for tighter integration of modules that were previously developed and optimized in isolation. Recent ground-breaking works have produced end-to-end deep network methods for both speech separation and end-to-end automatic speech ...
Shane Settle, Jonathan Le Roux, Takaaki Hori, Shinji Watanabe 0001, John R. Hershey   +4 more
openaire   +1 more source

An End-to-End Model for Vietnamese Speech Recognition

2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF), 2019
This paper presents an approach of End-to-End model based on Long Short-Term Memory (LSTM) and Time Delay Deep Neural Network (TDNN) models for Vietnamese speech recognition. Two Vietnamese End-to-End architectures using Connectionist Temporal Classification (CTC) as the loss function are proposed.
openaire   +1 more source

Speech Enhancement Using End-to-End Speech Recognition Objectives

2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019
Speech enhancement systems, which denoise and dereverberate distorted signals, are usually optimized based on signal reconstruction objectives including the maximum likelihood and minimum mean square error. However, emergent end-to-end neural methods enable to optimize the speech enhancement system with more application-oriented objectives. For example,
Aswin Shanmugam Subramanian, Xiaofei Wang 0007, Murali Karthick Baskar, Shinji Watanabe 0001, Toru Taniguchi, Dung T. Tran, Yuya Fujita   +6 more
openaire   +1 more source

End-to-End Accented Speech Recognition

Interspeech 2019, 2019
Thibault Viglino, Petr Motlícek, Milos Cernak   +2 more
openaire   +1 more source

Semi-Supervised End-to-End Speech Recognition

Interspeech 2018, 2018
Shigeki Karita, Shinji Watanabe 0001, Tomoharu Iwata, Atsunori Ogawa, Marc Delcroix   +4 more
openaire   +1 more source

End-to-End Audio-Visual Speech Recognition for Overlapping Speech

Interspeech 2021, 2021
Richard Rose, Olivier Siohan, Anshuman Tripathi, Otavio Braga   +3 more
openaire   +1 more source