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Found 15 papers, 2 papers with code
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2021, low-resource speech translation and multilingual speech translation.
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition
The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR.
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recognition (ASR).
Where are we in semantic concept extraction for Spoken Language Understanding?
More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing.
Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation
Boosted by the simultaneous translation shared task at IWSLT 2020, promising end-to-end online speech translation approaches were recently proposed.
An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies
This paper proposes a decoding strategy for end-to-end simultaneous speech translation.
End2End Acoustic to Semantic Transduction
In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism.
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
Pre-training for feature extraction is an increasingly studied approach to get better continuous representations of audio and text content.
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization
In order to build a corpus for this task, it is necessary to obtain the (automatic or manual) transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training.
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation
We report automatic alignment and summarization performances on this corpus and show that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation.
Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability
We present an end-to-end approach to extract semantic concepts directly from the speech audio signal.
End-to-end named entity extraction from speech
Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs.
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation
We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014.
automatic-speech-recognition
End-To-End Speech Recognition
+1
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems.