Distant Speech Recognition
10 papers with code • 2 benchmarks • 3 datasets
Libraries
Use these libraries to find Distant Speech Recognition models and implementationsLatest papers with no code
A network of deep neural networks for distant speech recognition
Despite the remarkable progress recently made in distant speech recognition, state-of-the-art technology still suffers from a lack of robustness, especially when adverse acoustic conditions characterized by non-stationary noises and reverberation are met.
End-to-end attention-based distant speech recognition with Highway LSTM
End-to-end attention-based models have been shown to be competitive alternatives to conventional DNN-HMM models in the Speech Recognition Systems.
Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models for Reverberant Speech Recognition
Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone.
Highway Long Short-Term Memory RNNs for Distant Speech Recognition
In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers.
The Sweet-Home speech and multimodal corpus for home automation interaction
Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes and Home Automation.
The DIRHA simulated corpus
This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA).