SpeechBrain: A general-purpose speech toolkit
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …
research and development of neural speech processing technologies by being simple …
A fine-tuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding
Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary
progress in Automatic Speech Recognition (ASR). However, they have not been totally …
progress in Automatic Speech Recognition (ASR). However, they have not been totally …
SLUE phase-2: A benchmark suite of diverse spoken language understanding tasks
Spoken language understanding (SLU) tasks have been studied for many decades in the
speech research community, but have not received as much attention as lower-level tasks …
speech research community, but have not received as much attention as lower-level tasks …
Match to win: Analysing sequences lengths for efficient self-supervised learning in speech and audio
Y Gaol, J Fernandez-Marques… - 2022 IEEE Spoken …, 2023 - ieeexplore.ieee.org
Self-supervised learning (SSL) has proven vital in speech and audio-related applications.
The paradigm trains a general model on unlabeled data that can later be used to solve …
The paradigm trains a general model on unlabeled data that can later be used to solve …
Finstreder: simple and fast spoken language understanding with finite state transducers using modern speech-to-text models
In Spoken Language Understanding (SLU) the task is to extract important information from
audio commands, like the intent of what a user wants the system to do and special entities …
audio commands, like the intent of what a user wants the system to do and special entities …
TARIC-SLU: A Tunisian Benchmark Dataset For Spoken Language Understanding
In recent years, there has been a significant increase in interest in developing Spoken
Language Understanding (SLU) systems. SLU involves extracting a list of semantic …
Language Understanding (SLU) systems. SLU involves extracting a list of semantic …
MSNER: A Multilingual Speech Dataset for Named Entity Recognition
While extensively explored in text-based tasks, Named Entity Recognition (NER) remains
largely neglected in spoken language understanding. Existing resources are limited to a …
largely neglected in spoken language understanding. Existing resources are limited to a …
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
Recently, deep end-to-end learning has been studied for intent classification in Spoken
Language Understanding (SLU). However, end-to-end models require a large amount of …
Language Understanding (SLU). However, end-to-end models require a large amount of …
Digits micro-model for accurate and secure transactions
Automatic Speech Recognition (ASR) systems are used in the financial domain to enhance
the caller experience by enabling natural language understanding and facilitating efficient …
the caller experience by enabling natural language understanding and facilitating efficient …
Deep neural networks for voice control
L Lugosch - 2023 - escholarship.mcgill.ca
Voice control systems enable people to control their computers by speaking to them. After a
review of the state-of-the-art in sequence modeling, speech recognition, and language …
review of the state-of-the-art in sequence modeling, speech recognition, and language …