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Found 8 papers, 2 papers with code
The RWTH Aachen LVCSR system for IWSLT-2016 German Skype conversation recognition task
In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT-2016 evaluation campaign are described.
4-bit Quantization of LSTM-based Speech Recognition Models
We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition (ASR): hybrid Deep Bidirectional LSTM - Hidden Markov Models (DBLSTM-HMMs) and Recurrent Neural Network - Transducers (RNN-Ts).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation independently.
On the limit of English conversational speech recognition
Compensation of the decoder model with the probability ratio approach allows more efficient integration of an external language model, and we report 5. 9% and 11. 5% WER on the SWB and CHM parts of Hub5'00 with very simple LSTM models.
Ranked #1 on
Speech Recognition
on Switchboard + Hub500
English Conversational Speech Recognition
Language Modelling
+1
RNN Transducer Models For Spoken Language Understanding
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Spoken Language Understanding Without Full Transcripts
For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts.
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard
It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition models are competitive with hybrid models only when a large amount of data, at least a thousand hours, is available for training.
Ranked #2 on
Speech Recognition
on swb_hub_500 WER fullSWBCH
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks.
