Papers with Mozilla Common Voice

3 papers
Samrómur: Crowd-sourcing large amounts of data (2022.lrec-1)

Copied to clipboard

Challenge: Samrómur is the largest prompted speech collection effort for Icelandic so far and verification is as monumental as the collection itself.
Approach: They propose to collect large and diverse corpus for automatic speech recognition and similar tools using crowd-sourced donations.
Outcome: The collected utterances are based on the Mozilla Common Voice platform and are available for free on the Samrómur collection platform.
Accented Speech Recognition With Accent-specific Codebooks (2023.emnlp-main)

Copied to clipboard

Challenge: Degradation in performance across underrepresented accents is a severe deterrent to inclusive adoption of ASR.
Approach: They propose an approach to adapt speech accents to unseen accents by using cross-attention with a trainable set of codebooks.
Outcome: The proposed approach yields significant performance gains on the seen English accents and unseen accents on the Mozilla Common Voice dataset.
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications (2020.lrec-1)

Copied to clipboard

Challenge: A speech technology exhibits demographic bias when performance is worse for one demographic group relative to another.
Approach: They create an English dataset of expert-validated audio, transcript> pairs with demographic tags for age, gender, accent and open software which may be used to detect demographic bias in Automatic Speech Recognition systems.
Outcome: The Artie Bias Corpus is a curated subset of the Mozilla Common Voice corpus, which is released under a Creative Commons CC0 license .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations