Spoken Language Understanding
114 papers with code • 5 benchmarks • 13 datasets
Libraries
Use these libraries to find Spoken Language Understanding models and implementationsDatasets
Most implemented papers
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label.
Learning Spoken Language Representations with Neural Lattice Language Modeling
Pre-trained language models have achieved huge improvement on many NLP tasks.
Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers.
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer.
ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models
In Spoken Language Understanding (SLU) the task is to extract important information from audio commands, like the intent of what a user wants the system to do and special entities like locations or numbers.
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models.
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data.
Sequential Dialogue Context Modeling for Spoken Language Understanding
We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history.