Language Model Data Augmentation for Keyword Spotting in Low-Resourced Training Conditions

Type de record:

paper

Créateur:

GorLilHuaLam

Éditeur:

LabVoc

Éditeur:

CCSD

Hôte:

International audience

Comparing decoding strategies for subword-based keyword spotting in low-resourced languages

International audience For languages with limited training resources, out-of-vocabula

Low-Resource Speech Recognition and Keyword-Spotting

The IARPA Babel program ran from March 2012 to November 2016. The aim of the program was to develop

Few-Shot Keyword Spotting in Any Language

We introduce a few-shot transfer learning method for keyword spotting in any language. Leveraging open speech corpora in nine languages, we automate the extraction of a large multilingual keyword bank and use it to train an embedding model. With just five training

MaiBERT: A Pre-training Corpus and Language Model for Low-Resourced Maithili Language

Natural Language Understanding (NLU) for low-resource languages remains a major challenge in NLP due

Feature learning for efficient ASR-free keyword spotting in low-resource languages

We consider feature learning for efficient keyword spotting that can be applied in severely under-re

Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments

One of the challenges in developing a high quality custom keyword spotting (KWS) model is the length

Language Model Data Augmentation for Keyword Spotting in Low-Resourced Training Conditions

Type de record:

Visit

Tasks