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Found 12 papers, 3 papers with code
CMU’s IWSLT 2022 Dialect Speech Translation System
We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.
SYSTRAN @ WMT 2021: Terminology Task
This paper describes SYSTRAN submissions to the WMT 2021 terminology shared task.
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification.
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
We show that further efficiency can be achieved with a vanilla HuBERT Base model, which can maintain 94% of XLS-R's performance with only 3% of the data, 4 GPUs, and limited trials.
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks.
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.
More Speaking or More Speakers?
We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Avoid Overthinking in Self-Supervised Models for Speech Recognition
Although popular for classification tasks in vision and language, EE has seen less use for sequence-to-sequence speech recognition (ASR) tasks where outputs from early layers are often degenerate.
Self-Supervised Learning
Sequence-To-Sequence Speech Recognition
+1
Continuous Pseudo-Labeling from the Start
Nevertheless, these approaches still rely on bootstrapping the ST using an initial supervised learning phase where the model is trained on labeled data alone.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation
The present work examines the assumption that combining non-learnable SF extractors to SSL models is an effective approach to low resource speech tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type.
