Search Results for author:
Found 10 papers, 1 papers with code
papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion
In this paper, we introduce papago - a translator for mobile device which is equipped with new features that can provide convenience for users.
Interactive Visualization and Manipulation of Attention-based Neural Machine Translation
While neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behavior.
Intermediate Loss Regularization for CTC-based Speech Recognition
In addition, we propose to combine this intermediate CTC loss with stochastic depth training, and apply this combination to a recently proposed Conformer network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Layer Pruning on Demand with Intermediate CTC
Deploying an end-to-end automatic speech recognition (ASR) model on mobile/embedded devices is a challenging task, since the device computational power and energy consumption requirements are dynamically changed in practice.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Memory-Efficient Training of RNN-Transducer with Sampled Softmax
RNN-Transducer has been one of promising architectures for end-to-end automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Better Intermediates Improve CTC Inference
This paper proposes a method for improved CTC inference with searched intermediates and multi-pass conditioning.
Large-scale learning of generalised representations for speaker recognition
We also show that training with proposed large data configurations gives better performance.
I3D: Transformer architectures with input-dependent dynamic depth for speech recognition
Transformer-based end-to-end speech recognition has achieved great success.
