241 citations · 392 across the 9 of their papers we have counts for
9 papers
Toward Joint Language Modeling for Speech Units and Text
Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu +5
Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of lan…
Scaling Speech Technology to 1,000+ Languages
Vineel Pratap, Andros Tjandra, Bowen Shi +13
Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to ab…
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
Alexei Baevski, Arun Babu, Wei-Ning Hsu +1
Current self-supervised learning algorithms are often modality-specific and require large amounts of computational resources. To address these issues, we increase the training effi…
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu +3
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single moda…
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
Arun Babu, Changhan Wang, Andros Tjandra +10
This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a mill…
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
David Eriksson, Pierce I-Jen Chuang, Samuel Daulton +7
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device l…
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Arun Babu, Akshat Shrivastava, Armen Aghajanyan +3
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespr…
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing
Akshat Shrivastava, Pierce Chuang, Arun Babu +4
An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance , predict…
Lightweight Convolutional Representations for On-Device Natural Language Processing
Shrey Desai, Geoffrey Goh, Arun Babu +1
The increasing computational and memory complexities of deep neural networks have made it difficult to deploy them on low-resource electronic devices (e.g., mobile phones, tablets,…