Spoken Language Understanding
130 papers with code • 5 benchmarks • 14 datasets
Benchmarks
These leaderboards are used to track progress in Spoken Language Understanding
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Libraries
Use these libraries to find Spoken Language Understanding models and implementationsDatasets
SLURP
Dialogue State Tracking Challenge
Spoken-SQuAD
xSID
MEDIA
Timers and Such
Most implemented papers
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Microsoft/SDNet
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Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
espnet/espnet
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Conformer has proven to be effective in many speech processing tasks.
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Reading comprehension has been widely studied.
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
lorenlugosch/end-to-end-SLU
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End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module.
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
MiuLab/SlotGated-SLU
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NAACL 2018
Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.
Spoken Language Understanding on the Edge
We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications.
Speech Model Pre-training for End-to-End Spoken Language Understanding
Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map speech directly to intent through a single trainable model.
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation
Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents.
