End-to-End Speech Processing Toolkit

A PyTorch-based Speech Toolkit

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

On-device Speech-to-Intent engine powered by deep learning

Open source code for EMNLP-19 Paper "A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding".

Open source code for EMNLP 2020 Findings Paper "AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling"

槽填充、意图预测（口语理解）论文整理和中文翻译。Slot filling and intent prediction paper collation and Chinese translation.

ICASSP 2023-2024 Papers: A complete collection of influential and exciting research papers from the ICASSP 2023-24 conferences. Explore the latest advancements in acoustics, speech and signal processing. Code included. Star the repository to support the advancement of audio and signal processing!

Spoken Language Understanding(SLU)/Slot Filling(语义槽填充) in PyTorch

ODSQA: OPEN-DOMAIN SPOKEN QUESTION ANSWERING DATASET

Source code for ASRU 2019 paper "Adapting Pretrained Transformer to Lattices for Spoken Language Understanding"

A spoken question answering dataset on SQUAD

Source code for ACL 2020 paper "Learning Spoken Language Representations with Neural Lattice Language Modeling"

Open source code and data for AAAI 2022 Oral Paper "Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding"

🦁 BERT for Spoken Language Understanding in Task-based Dialog

Semi-supervised spoken language understanding (SLU) via self-supervised speech and language model pretraining

real time japanese speech recognition translator using wav2vec2

Learning a common representation space from speech and text for cross-modal retrieval given textual queries and speech files.

A TensorFlow implement for "A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding".

This repository is a comprehensive project that leverages the XLM-Roberta model for intent detection. This repository is a valuable resource for developers looking to build and fine-tune intent detection models based on state-of-the-art techniques.