RNN for Spoken Language Understanding
-
Updated
Jul 12, 2017 - Python
RNN for Spoken Language Understanding
All NLP related courses on DataCamp
Code for the paper "Learning English with Peppa Pig" https://doi.org/10.48550/arXiv.2202.12917
This project demonstrates the use of generic bi-directional LSTM models for predicting importance of words in a spoken dialgoue for understanding its meaning. The model operates on human-annotated corpus of word importance for its training and evaluation. The corpus can be downloaded from: http://latlab.ist.rit.edu/lrec2018
Convex combination of phonotactics for large-scale spoken language identification
This is an implementation of paper "End-to-end Speech Translation via Cross-modal Progressive Training" (Interspeech2021)
Example codes for my PhD work on recognizing dimensional emotions in spoken dialogue
software that analyzes speech utterances
10 digits recognition system based on DTW, HMM and GMM
code for paper "Cross-modal Contrastive Learning for Speech Translation" (NAACL 2022)
A PyPI package for fast word/character error rate (WER/CER) calculation
Add a description, image, and links to the spoken-language-processing topic page so that developers can more easily learn about it.
To associate your repository with the spoken-language-processing topic, visit your repo's landing page and select "manage topics."