Search Results for author:
Found 12 papers, 3 papers with code
Anatomy of Industrial Scale Multilingual ASR
This paper describes AssemblyAI's industrial-scale automatic speech recognition (ASR) system, designed to meet the requirements of large-scale, multilingual ASR serving various application needs.
Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping
This paper presents Conformer-1, an end-to-end Automatic Speech Recognition (ASR) model trained on an extensive dataset of 570k hours of speech audio data, 91% of which was acquired from publicly available sources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Going for GOAL: A Resource for Grounded Football Commentaries
As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding.
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games
Guessing games are a prototypical instance of the "learning by interacting" paradigm.
Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks
We study the problem of integrating syntactic information from constituency trees into a neural model in Frame-semantic parsing sub-tasks, namely Target Identification (TI), FrameIdentification (FI), and Semantic Role Labeling (SRL).
SLURP: A Spoken Language Understanding Resource Package
Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications.
Ranked #3 on
Slot Filling
on SLURP
(using extra training data)
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time.
CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning
To remedy this, we present GROLLA, an evaluation framework for Grounded Language Learning with Attributes with three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation.
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU
We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems.
MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces
In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall.
Structured Learning for Context-aware Spoken Language Understanding of Robotic Commands
Service robots are expected to operate in specific environments, where the presence of humans plays a key role.
