Search Results for author:
Found 8 papers, 4 papers with code
Breeze-7B Technical Report
Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Chinese.
Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite
In an effort to advance the evaluation of language models in Traditional Chinese and stimulate further research in this field, we have open-sourced our benchmark and opened the model for trial.
Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning
In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt.
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese.
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR
To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5
In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e. g. HuBERT) and pretrained language models (PLM, e. g. T5).
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding
Transformer-based models are widely used in natural language understanding (NLU) tasks, and multimodal transformers have been effective in visual-language tasks.
Analyzing the Robustness of Unsupervised Speech Recognition
In this work, we further analyze the training robustness of unsupervised ASR on the domain mismatch scenarios in which the domains of unpaired speech and text are different.
