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Found 15 papers, 6 papers with code
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).
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.
Batch-normalized joint training for DNN-based distant speech recognition
Improving distant speech recognition is a crucial step towards flexible human-machine interfaces.
Improving speech recognition by revising gated recurrent units
First, we suggest to remove the reset gate in the GRU design, resulting in a more efficient single-gate architecture.
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments
This paper introduces the contents and the possible usage of the DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA project.
Contaminated speech training methods for robust DNN-HMM distant speech recognition
Despite the significant progress made in the last years, state-of-the-art speech recognition technologies provide a satisfactory performance only in the close-talking condition.
Realistic multi-microphone data simulation for distant speech recognition
The availability of realistic simulated corpora is of key importance for the future progress of distant speech recognition technology.
Audio and Speech Processing Sound
Light Gated Recurrent Units for Speech Recognition
A field that has directly benefited from the recent advances in deep learning is Automatic Speech Recognition (ASR).
Ranked #6 on
Speech Recognition
on TIMIT
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Automatic context window composition for distant speech recognition
Distant speech recognition is being revolutionized by deep learning, that has contributed to significantly outperform previous HMM-GMM systems.
DiPCo -- Dinner Party Corpus
We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment.
Sample Drop Detection for Distant-speech Recognition with Asynchronous Devices Distributed in Space
The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step.
Sound Audio and Speech Processing I.2.7
Multi-Channel Transformer Transducer for Speech Recognition
In this paper, we present a novel speech recognition model, Multi-Channel Transformer Transducer (MCTT), which features end-to-end multi-channel training, low computation cost, and low latency so that it is suitable for streaming decoding in on-device speech recognition.
Context-Aware Transformer Transducer for Speech Recognition
We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A neural prosody encoder for end-ro-end dialogue act classification
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems.
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition
We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion.
