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Recent progress in the CUHK dysarthric speech recognition system
Despite the rapid progress of automatic speech recognition (ASR) technologies in the past
few decades, recognition of disordered speech remains a highly challenging task to date …
few decades, recognition of disordered speech remains a highly challenging task to date …
Trends and developments in automatic speech recognition research
This paper discusses how automatic speech recognition systems are and could be
designed, in order to best exploit the discriminative information encoded in human speech …
designed, in order to best exploit the discriminative information encoded in human speech …
Wake word detection with streaming transformers
Modern wake word detection systems usually rely on neural networks for acoustic modeling.
Transformers has recently shown superior performance over LSTM and convolutional …
Transformers has recently shown superior performance over LSTM and convolutional …
CTC variations through new WFST topologies
This paper presents novel Weighted Finite-State Transducer (WFST) topologies to
implement Connectionist Temporal Classification (CTC)-like algorithms for automatic …
implement Connectionist Temporal Classification (CTC)-like algorithms for automatic …
Principled comparisons for end-to-end speech recognition: Attention vs hybrid at the 1000-hour scale
End-to-End speech recognition has become the center of attention for speech recognition
research, but Hybrid Hidden Markov Model Deep Neural Network (HMM/DNN)-systems …
research, but Hybrid Hidden Markov Model Deep Neural Network (HMM/DNN)-systems …
Finnish parliament ASR corpus: Analysis, benchmarks and statistics
Public sources like parliament meeting recordings and transcripts provide ever-growing
material for the training and evaluation of automatic speech recognition (ASR) systems. In …
material for the training and evaluation of automatic speech recognition (ASR) systems. In …
Audio-visual multi-channel integration and recognition of overlapped speech
Automatic speech recognition (ASR) technologies have been significantly advanced in the
past few decades. However, recognition of overlapped speech remains a highly challenging …
past few decades. However, recognition of overlapped speech remains a highly challenging …
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model
In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the
overfitting issues with connectionist temporal classification (CTC) training to reduce its …
overfitting issues with connectionist temporal classification (CTC) training to reduce its …
Pkwrap: a pytorch package for lf-mmi training of acoustic models
We present a simple wrapper that is useful to train acoustic models in PyTorch using Kaldi's
LF-MMI training framework. The wrapper, called pkwrap (short form of PyTorch kaldi …
LF-MMI training framework. The wrapper, called pkwrap (short form of PyTorch kaldi …
Wake word detection with alignment-free lattice-free MMI
Always-on spoken language interfaces, eg personal digital assistants, rely on a wake word
to start processing spoken input. We present novel methods to train a hybrid DNN/HMM …
to start processing spoken input. We present novel methods to train a hybrid DNN/HMM …