This paper presents XLS-R, a large-scale model for cross-lingual speech representation
learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a …

Despite rapid progress in the recent past, current speech recognition systems still require
labeled training data which limits this technology to a small fraction of the languages spoken …

This paper presents XLSR which learns cross-lingual speech representations by pretraining
a single model from the raw waveform of speech in multiple languages. We build on …

Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been
extensively investigated in the supervised setting. This assumes the existence of a parallel …

Unsupervised speech recognition has shown great potential to make Automatic Speech
Recognition (ASR) systems accessible to every language. However, existing methods still …

Abstract Language Identification (LI) is one of the widely emerging field in the areas of
speech processing to accurately identify the language from the data base based on some …

Self-supervised speech representation learning (speech SSL) has demonstrated the benefit
of scale in learning rich representations for Automatic Speech Recognition (ASR) with …

In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as
common. The complexity of the language might be one of the main reasons for this latency …

Stuttering is a neuro-developmental speech impairment characterized by uncontrolled
utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is …

Humans learn language by interaction with their environment and listening to other humans.
It should also be possible for computational models to learn language directly from speech …