Search Results for author: Isabel Trancoso

Found 38 papers, 4 papers with code

QualityAdapt: an Automatic Dialogue Quality Estimation Framework

1 code implementation SIGDIAL (ACL) 2022 John Mendonca, Alon Lavie, Isabel Trancoso

Despite considerable advances in open-domain neural dialogue systems, their evaluation remains a bottleneck.

Towards Speaker Verification for Crowdsourced Speech Collections

no code implementations LREC 2022 John Mendonca, Rui Correia, Mariana Lourenço, João Freitas, Isabel Trancoso

Crowdsourcing the collection of speech provides a scalable setting to access a customisable demographic according to each dataset’s needs.

Speaker Verification

Privacy-preserving Automatic Speaker Diarization

no code implementations26 Oct 2022 Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso

Automatic Speaker Diarization (ASD) is an enabling technology with numerous applications, which deals with recordings of multiple speakers, raising special concerns in terms of privacy.

Privacy Preserving speaker-diarization +1

Towards End-to-End Private Automatic Speaker Recognition

no code implementations23 Jun 2022 Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso

This poses two important issues: first, knowledge of the speaker embedding extraction model may create security and robustness liabilities for the authentication system, as this knowledge might help attackers in crafting adversarial examples able to mislead the system; second, from the point of view of a service provider the speaker embedding extraction model is arguably one of the most valuable components in the system and, as such, disclosing it would be highly undesirable.

Privacy Preserving Speaker Recognition +1

Using Self-Supervised Feature Extractors with Attention for Automatic COVID-19 Detection from Speech

no code implementations30 Jun 2021 John Mendonça, Rubén Solera-Ureña, Alberto Abad, Isabel Trancoso

Experimental results demonstrate that models trained on features extracted from self-supervised models perform similarly or outperform fully-supervised models and models based on handcrafted features.

Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning

no code implementations LREC 2020 Joana Correia, Isabel Trancoso, Bhiksha Raj

The automation of the diagnosis and monitoring of speech affecting diseases in real life situations, such as Depression or Parkinson{'}s disease, depends on the existence of rich and large datasets that resemble real life conditions, such as those collected from in-the-wild multimedia repositories like YouTube.

Multiple Instance Learning

Pathological speech detection using x-vector embeddings

no code implementations2 Mar 2020 Catarina Botelho, Francisco Teixeira, Thomas Rolland, Alberto Abad, Isabel Trancoso

We test our approach against knowledge-based features and i-vectors, and report results for two European Portuguese corpora, for OSA and PD, as well as for an additional Spanish corpus for PD.

Assessing User Expertise in Spoken Dialog System Interactions

no code implementations18 Jan 2017 Eugénio Ribeiro, Fernando Batista, Isabel Trancoso, José Lopes, Ricardo Ribeiro, David Martins de Matos

Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques.

Character-based Neural Machine Translation

no code implementations14 Nov 2015 Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. black

We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words.

Machine Translation Translation

Privacy-Preserving Multi-Document Summarization

no code implementations6 Aug 2015 Luís Marujo, José Portêlo, Wang Ling, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell, Isabel Trancoso, Bhiksha Raj

State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties.

Document Summarization Multi-Document Summarization +1

Linguistic Evaluation of Support Verb Constructions by OpenLogos and Google Translate

no code implementations LREC 2014 Anabela Barreiro, Johanna Monti, Brigitte Orliac, Susanne Preu{\ss}, Kutz Arrieta, Wang Ling, Fern Batista, o, Isabel Trancoso

This paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system (Google Translate) for five languages: French, German, Italian, Portuguese and Spanish.

Machine Translation Translation

Revising the annotation of a Broadcast News corpus: a linguistic approach

no code implementations LREC 2014 Vera Cabarr{\~a}o, Helena Moniz, Fern Batista, o, Ricardo Ribeiro, Nuno Mamede, Hugo Meinedo, Isabel Trancoso, Ana Isabel Mata, David Martins de Matos

This paper presents a linguistic revision process of a speech corpus of Portuguese broadcast news focusing on metadata annotation for rich transcription, and reports on the impact of the new data on the performance for several modules.

speech-recognition Speech Recognition

OpenLogos Semantico-Syntactic Knowledge-Rich Bilingual Dictionaries

no code implementations LREC 2014 Anabela Barreiro, Fern Batista, o, Ricardo Ribeiro, Helena Moniz, Isabel Trancoso

This paper presents 3 sets of OpenLogos resources, namely the English-German, the English-French, and the English-Italian bilingual dictionaries.

Machine Translation Translation

Dealing with unknown words in statistical machine translation

no code implementations LREC 2012 Jo{\~a}o Silva, Lu{\'\i}sa Coheur, {\^A}ngela Costa, Isabel Trancoso

In Statistical Machine Translation, words that were not seen during training are unknown words, that is, words that the system will not know how to translate.

Translation Transliteration

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