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Found 16 papers, 3 papers with code
Improved Cross-Lingual Transfer Learning For Automatic Speech Translation
Having a single model that supports multiple translation tasks is desirable.
Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages
Recent models such as XLS-R and Whisper have made multilingual speech technologies more accessible by pre-training on audio from around 100 spoken languages each.
On Unsupervised Uncertainty-Driven Speech Pseudo-Label Filtering and Model Calibration
Dropout-based Uncertainty-driven Self-Training (DUST) proceeds by first training a teacher model on source domain labeled data.
SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation
We combine state-of-the-art multilingual acoustic frame-level speech representation learning model XLS-R with the Language Agnostic BERT Sentence Embedding (LaBSE) model to create an utterance-level multimodal multilingual speech encoder SAMU-XLSR.
CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification
Audio classification is an active research area with a wide range of applications.
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0
We propose a simple and effective cross-lingual transfer learning method to adapt monolingual wav2vec-2. 0 models for Automatic Speech Recognition (ASR) in resource-scarce languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
We investigate the existence of sparse subnetworks in pre-trained speech SSL models that achieve even better low-resource ASR results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training
The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning
The audio encoder is trained to perform a speech-translation retrieval task in a contrastive learning framework.
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
Probabilistic Latent Variable Models (LVMs) provide an alternative to self-supervised learning approaches for linguistic representation learning from speech.
Robust Training of Vector Quantized Bottleneck Models
We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs.
DARTS: Dialectal Arabic Transcription System
We analyze the following; transfer learning from high resource broadcast domain to low-resource dialectal domain and semi-supervised learning where we use in-domain unlabeled audio data collected from YouTube.
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
QCRI Live Speech Translation System
This paper presents QCRI{'}s Arabic-to-English live speech translation system.
Multi-view Dimensionality Reduction for Dialect Identification of Arabic Broadcast Speech
In this work, we present a new Vector Space Model (VSM) of speech utterances for the task of spoken dialect identification.
Automatic Dialect Detection in Arabic Broadcast Speech
We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%.
