Spoken Language Understanding

61 papers with code • 1 benchmarks • 7 datasets

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Greatest papers with code

ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet

espnet/espnet 29 Nov 2021

However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.

Language understanding Spoken Language Understanding

Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

snipsco/snips-nlu 25 May 2018

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.

automatic-speech-recognition Language understanding +3

Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers

speechbrain/speechbrain 4 Apr 2021

This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers.

Language understanding Spoken Language Understanding

A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

yizhen20133868/Awesome-SLU-Survey 4 Mar 2021

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system.

Language understanding Spoken Language Understanding

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

MiuLab/SlotGated-SLU 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.

Global Optimization Intent Detection +3

Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models

lorenlugosch/pretrain_speech_model 21 Oct 2019

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.

Data Augmentation Language understanding +3

Speech Model Pre-training for End-to-End Spoken Language Understanding

lorenlugosch/end-to-end-SLU 7 Apr 2019

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.

Ranked #2 on Spoken Language Understanding on Fluent Speech Commands (using extra training data)

Language understanding Spoken Language Understanding

From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding

Kaleidophon/deep-significance NAACL 2021

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.

Intent Classification Intent Detection +6

Fully Statistical Neural Belief Tracking

nmrksic/neural-belief-tracker ACL 2018

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).

Dialogue Management Dialogue State Tracking +2

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

LeePleased/StackPropagation-SLU IJCNLP 2019

In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.

Intent Detection Language understanding +2