33 citations · 121 across the 12 of their papers we have counts for
17 papers
Not Quite My Tempo: Voice Activity-aware Speech Synthesis for Lip-Synchronous Dubbing
Alejandro Pérez-González-de-Martos, Florian Lux, Angelina Elizarova +3
Automatic lip-synchronous dubbing requires a speech synthesis model to generate alternating voice and silence patterns in the target language that match the timing of the source cl…
Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text
Parnia Bahar, Mattia Di Gangi, Nick Rossenbach +1
Automatic Arabic diacritization is useful in many applications, ranging from reading support for language learners to accurate pronunciation predictor for downstream tasks like spe…
On Knowledge Distillation for Direct Speech Translation
Marco Gaido, Mattia A. Di Gangi, Matteo Negri +1
Direct speech translation (ST) has shown to be a complex task requiring knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and machine translation (MT). For…
On Target Segmentation for Direct Speech Translation
Mattia Antonino Di Gangi, Marco Gaido, Matteo Negri +1
Recent studies on direct speech translation show continuous improvements by means of data augmentation techniques and bigger deep learning models. While these methods are helping t…
Contextualized Translation of Automatically Segmented Speech
Marco Gaido, Mattia Antonino Di Gangi, Matteo Negri +2
Direct speech-to-text translation (ST) models are usually trained on corpora segmented at sentence level, but at inference time they are commonly fed with audio split by a voice ac…
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Luisa Bentivogli, Beatrice Savoldi, Matteo Negri +3
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to t…
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020
Marco Gaido, Mattia Antonino Di Gangi, Matteo Negri +1
This paper describes FBK's participation in the IWSLT 2020 offline speech translation (ST) task. The task evaluates systems' ability to translate English TED talks audio into Germa…
Controlling the Output Length of Neural Machine Translation
Surafel Melaku Lakew, Mattia Di Gangi, Marcello Federico
The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be…
Instance-Based Model Adaptation For Direct Speech Translation
Mattia Antonino Di Gangi, Viet-Nhat Nguyen, Matteo Negri +1
Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available traini…
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
Mattia Antonino Di Gangi, Robert Enyedi, Alessandra Brusadin +1
Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is no…
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman, Orhan Firat, Mattia A. Di Gangi +2
Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and…
One-To-Many Multilingual End-to-end Speech Translation
Mattia Antonino Di Gangi, Matteo Negri, Marco Turchi
Nowadays, training end-to-end neural models for spoken language translation (SLT) still has to confront with extreme data scarcity conditions. The existing SLT parallel corpora are…
Multilingual Neural Machine Translation for Zero-Resource Languages
Surafel M. Lakew, Marcello Federico, Matteo Negri +1
In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine t…
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
Nicholas Ruiz, Mattia Antonino Di Gangi, Nicola Bertoldi +1
Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language. While the evaluation of neural machine transl…
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
Mattia Antonino Di Gangi, Giosué Lo Bosco, Giovanni Pilato
Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for aut…
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018
Mattia Antonino Di Gangi, Roberto Dessì, Roldano Cattoni +2
This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs,…
Deep Neural Machine Translation with Weakly-Recurrent Units
Mattia Antonino Di Gangi, Marcello Federico
Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation. Recently, new architectures have been proposed, which can leverage p…