Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation
learning. It follows a two-stage training process of pre-training and fine-tuning, and performs …

Automatic spoken language identification (LID) is a very important research field in the era of
multilingual voice-command-based human-computer interaction. A front-end LID module …

We propose two end-to-end neural configurations for language diarization on bilingual code-
switching speech. The first, a BLSTM-E2E architecture, includes a set of stacked …

The task of automatic language identification (LID) involving multiple dialects of the same
language family in the presence of noise is a challenging problem. In these scenarios, the …

Attention-based convolutional neural network models are increasingly adopted for language
recognition tasks. In this paper, based on the self-attention mechanism, we solve the study of …

Spoken language identification (LID) technologies have improved in recent years from
discriminating largely distinct languages to discriminating highly similar languages or even …

In this paper, we conduct one of the very first studies for cross-corpora performance
evaluation in the spoken language identification (LID) problem. Cross-corpora evaluation …

Identifying language information from speech utterance is referred to as spoken language
identification. Language Identification (LID) is essential in multilingual speech systems. The …

Current end-to-end automatic speech recognition (ASR) models have achieved good results
in phonetic language such as English and French. However, Chinese character is a typical …

Accent-variation is a challenging issue, either for traditional hybrid or current end-to-end
(E2E) automatic speech recognition (ASR). Building an accent-invariant and high quality …