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Found 42 papers, 7 papers with code
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools
In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works.
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding
Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
Towards Early Prediction of Self-Supervised Speech Model Performance
Results show that measures of cluster quality and rank correlate better with downstream performance than the pre-training loss with only one hour of unlabeled audio, reducing the need for GPU hours and labeled data in SSL model evaluation.
An Analysis of Linear Complexity Attention Substitutes with BEST-RQ
Self-Supervised Learning (SSL) has proven to be effective in various domains, including speech processing.
Automatic Voice Identification after Speech Resynthesis using PPG
Speech resynthesis is a generic task for which we want to synthesize audio with another audio as input, which finds applications for media monitors and journalists. Among different tasks addressed by speech resynthesis, voice conversion preserves the linguistic information while modifying the identity of the speaker, and speech edition preserves the identity of the speaker but some words are modified. In both cases, we need to disentangle speaker and phonetic contents in intermediate representations. Phonetic PosteriorGrams (PPG) are a frame-level probabilistic representation of phonemes, and are usually considered speaker-independent. This paper presents a PPG-based speech resynthesis system. A perceptive evaluation assesses that it produces correct audio quality. Then, we demonstrate that an automatic speaker verification model is not able to recover the source speaker after re-synthesis with PPG, even when the model is trained on synthetic data.
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition
In this work, we detail our submission to the 2024 edition of the MSP-Podcast Speech Emotion Recognition (SER) Challenge.
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation
Recent advancements in textless speech-to-speech translation systems have been driven by the adoption of self-supervised learning techniques.
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect
In contrast to existing research, this paper contributes by comparing the effectiveness of SSL approaches in the context of (i) the low-resource spoken Tunisian Arabic dialect and (ii) its combination with a low-resource SLU and ASR scenario, where only a few semantic annotations are available for fine-tuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets
In spoken Task-Oriented Dialogue (TOD) systems, the choice of the semantic representation describing the users' requests is key to a smooth interaction.
A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding
Self-Supervised Learning is vastly used to efficiently represent speech for Spoken Language Understanding, gradually replacing conventional approaches.
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants
Recent works demonstrate that voice assistants do not perform equally well for everyone, but research on demographic robustness of speech technologies is still scarce.
Open Implementation and Study of BEST-RQ for Speech Processing
BERT-based Speech pre-Training with Random-projection Quantizer (BEST-RQ), is an SSL method that has shown great performance on Automatic Speech Recognition (ASR) while being simpler than other SSL methods, such as wav2vec 2. 0.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Is one brick enough to break the wall of spoken dialogue state tracking?
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's requests (\textit{a. k. a} dialogue state tracking) is key to a smooth interaction.
Enhancing expressivity transfer in textless speech-to-speech translation
Textless speech-to-speech translation systems are rapidly advancing, thanks to the integration of self-supervised learning techniques.
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations
In the context of telephone conversations, we can break down the audio information into acoustic and linguistic by using the speech signal and its transcription.
Semantic enrichment towards efficient speech representations
Over the past few years, self-supervised learned speech representations have emerged as fruitful replacements for conventional surface representations when solving Spoken Language Understanding (SLU) tasks.
Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data
We propose a method for speech-to-speech emotionpreserving translation that operates at the level of discrete speech units.
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset
Automatic speech recognition (ASR) systems become increasingly efficient thanks to new advances in neural network training like self-supervised learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
OLISIA: a Cascade System for Spoken Dialogue State Tracking
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language. In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving Accented Speech Recognition with Multi-Domain Training
Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets.
Federated Learning for ASR based on Wav2vec 2.0
This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2. 0 model pre-trained by self supervision.
On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding
In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU).
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems
These models are pre-trained on unlabeled audio data and then used in speech processing downstream tasks such as automatic speech recognition (ASR) or speech translation (ST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
End-to-end model for named entity recognition from speech without paired training data
Our approach is based on the use of an external model trained to generate a sequence of vectorial representations from text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Speech Resources in the Tamasheq Language
In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger.
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).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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
Automatic Speech Recognition (ASR)
+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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
