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Found 13 papers, 5 papers with code
Low-Resource Self-Supervised Learning with SSL-Enhanced TTS
Self-supervised learning (SSL) techniques have achieved remarkable results in various speech processing tasks.
Scaling Speech Technology to 1,000+ Languages
Expanding the language coverage of speech technology has the potential to improve access to information for many more people.
STOP: A dataset for Spoken Task Oriented Semantic Parsing
Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated version to benchmark the performance for low-resource domain adaptation of end-to-end SLU systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Generative Spoken Dialogue Language Modeling
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues.
textless-lib: a Library for Textless Spoken Language Processing
Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing
We tackle this problem by introducing scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" (an intent-slot template with variable leaf spans) before generating its frame, complete with ontology and utterance tokens.
POS Tagging for Improving Code-Switching Identification in Arabic
When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language.
Segmentation for Domain Adaptation in Arabic
Segmentation serves as an integral part in many NLP applications including Machine Translation, Parsing, and Information Retrieval.
A Challenge Set and Methods for Noun-Verb Ambiguity
English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97{\%}+ accuracy on the WSJ Penn Treebank since 2002.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model.
