An empirical evaluation shows that this substantially outperforms the cascaded and direct approaches and a previously used two-stage model in favorable data conditions, and is

Previous work on sequence-to-sequence speech translation has used encoder downsampling of 4 × , while 8 × is more common among sequence-to- sequence ASR systems ( Zhang et al. , 2017

We use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with dis- fluencies removed using the recently collected ‘copy-edited’ references for

Motivated by this contiguity, we propose an SLT adaptation of Transformer (the state-of-the-art architecture in MT), which exploits the integration of ASR solutions to cope with

We have also introduced a novel objective function that allows the network to be directly optimised for word error rate, and shown how to integrate the net- work outputs with a

This project shows that the End-to-End Speech Translation system outperforms the con- catenation of Speech Recognition and Machine Translation systems, when all systems are

Task 3, using end-to-end speech to text translation, allows one to ignore the transcription correction process and proceed directly to post-editing the speech translation, and

Operator characteristics–residence, age, race, occupation, off-farm work, sex, Spanish, Hispanic, or Latino origin, years on

Key words: Deep learning, automatic speech recognition, end-to-end training, convolutional neural networks, raw speech signal, robust speech recognition, conditional random

We hypothesize that such a model may help to address the identified data efficiency issue: Unlike multi-task training for the direct model that trains auxiliary models on

In this paper, we extend the work of [10], which mainly works for small databases, using ResNets as proposed in [13]. To the best of our knowledge, this is the first end- to-end

In this paper, we extend the work of [10], which mainly works for small databases, using ResNets as proposed in [13]. To the best of our knowledge, this is the first end- to-end

20060925 • TP6251-01 Manta Test Systems; Time Synchronized End-to-End Testing of Transmission & Distribution Line Protections with the MTS-5000 Application Note: AN506 Manta

The model factors, for example, directly indicate whether an absent coreference link is due to low mention scores (for either span) or a low score from the mention ranking

Towards the goal of automatic text understanding, machine learning models are expected to accurately extract potentially ambiguous mentions of enti- ties from a textual document

End-to-end training makes the neural ma- chine translation (NMT) architecture sim- pler, yet elegant compared to traditional statistical machine translation (SMT). However, little

To our opinion, to evaluate the performance of a complete speech-to-speech translation system, we need to compare the source speech used as input to the translated output speech in