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Found 49 papers, 9 papers with code
Towards Speaker Verification for Crowdsourced Speech Collections
Crowdsourcing the collection of speech provides a scalable setting to access a customisable demographic according to each dataset’s needs.
QualityAdapt: an Automatic Dialogue Quality Estimation Framework
Despite considerable advances in open-domain neural dialogue systems, their evaluation remains a bottleneck.
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems
In this paper, we present a bias and sustainability focused investigation of Automatic Speech Recognition (ASR) systems, namely Whisper and Massively Multilingual Speech (MMS), which have achieved state-of-the-art (SOTA) performances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge
This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks.
Speech as a Biomarker for Disease Detection
The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.
Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs
Although human evaluation remains the gold standard for open-domain dialogue evaluation, the growing popularity of automated evaluation using Large Language Models (LLMs) has also extended to dialogue.
ECoh: Turn-level Coherence Evaluation for Multilingual Dialogues
Motivated by the need for lightweight, open source, and multilingual dialogue evaluators, this paper introduces GenResCoh (Generated Responses targeting Coherence).
On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation
Large Language Models (LLMs) have showcased remarkable capabilities in various Natural Language Processing tasks.
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features
Membership Inference (MI) poses a substantial privacy threat to the training data of Automatic Speech Recognition (ASR) systems, while also offering an opportunity to audit these models with regard to user data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dialogue Quality and Emotion Annotations for Customer Support Conversations
Task-oriented conversational datasets often lack topic variability and linguistic diversity.
Privacy-oriented manipulation of speaker representations
Speaker embeddings are ubiquitous, with applications ranging from speaker recognition and diarization to speech synthesis and voice anonymisation.
Simple LLM Prompting is State-of-the-Art for Robust and Multilingual Dialogue Evaluation
Despite significant research effort in the development of automatic dialogue evaluation metrics, little thought is given to evaluating dialogues other than in English.
Towards Multilingual Automatic Dialogue Evaluation
The main limiting factor in the development of robust multilingual dialogue evaluation metrics is the lack of multilingual data and the limited availability of open sourced multilingual dialogue systems.
Privacy-preserving Automatic Speaker Diarization
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.
Towards End-to-End Private Automatic Speaker Recognition
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.
Using Self-Supervised Feature Extractors with Attention for Automatic COVID-19 Detection from Speech
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.
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
Speaker identification models are vulnerable to carefully designed adversarial perturbations of their input signals that induce misclassification.
Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning
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.
Pathological speech detection using x-vector embeddings
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
Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques.
SPA: Web-based Platform for easy Access to Speech Processing Modules
This paper presents SPA, a web-based Speech Analytics platform that integrates several speech processing modules and that makes it possible to use them through the web.
Character-based Neural Machine Translation
We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words.
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs.
Ranked #4 on
Part-Of-Speech Tagging
on Penn Treebank
Privacy-Preserving Multi-Document Summarization
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.
Towards Using Machine Translation Techniques to Induce Multilingual Lexica of Discourse Markers
Discourse markers are universal linguistic events subject to language variation.
Automatic Fado Music Classification
In late 2011, Fado was elevated to the oral and intangible heritage of humanity by UNESCO.
OpenLogos Semantico-Syntactic Knowledge-Rich Bilingual Dictionaries
This paper presents 3 sets of OpenLogos resources, namely the English-German, the English-French, and the English-Italian bilingual dictionaries.
Linguistic Evaluation of Support Verb Constructions by OpenLogos and Google Translate
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.
Revising the annotation of a Broadcast News corpus: a linguistic approach
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.
Dealing with unknown words in statistical machine translation
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.
