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Found 10 papers, 5 papers with code
BAT: Learning to Reason about Spatial Sounds with Large Language Models
By integrating Spatial-AST with LLaMA-2 7B model, BAT transcends standard Sound Event Localization and Detection (SELD) tasks, enabling the model to reason about the relationships between the sounds in its environment.
EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
Audio self-supervised learning (SSL) pre-training, which aims to learn good representations from unlabeled audio, has made remarkable progress.
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
To the best of our knowledge, emotion2vec is the first universal representation model in various emotion-related tasks, filling a gap in the field.
Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation Learning
Recent years have witnessed significant advancements in self-supervised learning (SSL) methods for speech-processing tasks.
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition
In this paper, we explored how to boost speech emotion recognition (SER) with the state-of-the-art speech pre-trained model (PTM), data2vec, text generation technique, GPT-4, and speech synthesis technique, Azure TTS.
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition
In recent years, speech-based self-supervised learning (SSL) has made significant progress in various tasks, including automatic speech recognition (ASR).
Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation
Our models outperform other SSL models significantly on the LibriSpeech benchmark without the need for iterative re-clustering and re-training.
Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition
However, the training of SSL models is computationally expensive and a common practice is to fine-tune a released SSL model on the specific task.
MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets
In this paper, we provide a new perspective on self-supervised speech models from how the training targets are obtained.
Ranked #40 on
Speech Recognition
on LibriSpeech test-other
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition
Recent years have witnessed great strides in self-supervised learning (SSL) on the speech processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
