spoken-language-understanding

A PyTorch-based Speech Toolkit

End-to-End Speech Processing Toolkit

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

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!

Plug-and-play streaming semantic VAD for real-time full-duplex spoken dialogue systems.

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

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"

Sylber: Syllabic Embedding Representation of Speech from Raw Audio

ODSQA: OPEN-DOMAIN SPOKEN QUESTION ANSWERING DATASET

A spoken question answering dataset on SQUAD

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.

Dataset Release for Intent Classification from Speech

real time japanese speech recognition translator using wav2vec2

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

A general purpose task-agnostic speech augmentation policy

EMNLP-2020: Cross-lingual Spoken Language Understanding with Regularized Representation Alignment

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

FastLongSpeech is a novel framework designed to extend the capabilities of Large Speech-Language Models for efficient long-speech processing without necessitating dedicated long-speech training data.