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Found 5 papers, 5 papers with code
MERLIon CCS Challenge Evaluation Plan
This paper introduces the inaugural Multilingual Everyday Recordings- Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge, focused on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom.
Investigating model performance in language identification: beyond simple error statistics
These overview metrics do not provide information about model performance at the level of individual speakers, recordings, or units of speech with different linguistic characteristics.
MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization
To enhance the reliability and robustness of language identification (LID) and language diarization (LD) systems for heterogeneous populations and scenarios, there is a need for speech processing models to be trained on datasets that feature diverse language registers and speech patterns.
PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification
We propose a novel model to hierarchically incorporate phoneme and phonotactic information for language identification (LID) without requiring phoneme annotations for training.
Enhance Language Identification using Dual-mode Model with Knowledge Distillation
In this paper, we propose to employ a dual-mode framework on the x-vector self-attention (XSA-LID) model with knowledge distillation (KD) to enhance its language identification (LID) performance for both long and short utterances.
