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Found 26 papers, 11 papers with code
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge
Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice synthesis (SVS).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MuPT: A Generative Symbolic Music Pretrained Transformer
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music.
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs.
3D-Speaker-Toolkit: An Open Source Toolkit for Multi-modal Speaker Verification and Diarization
This paper introduces 3D-Speaker-Toolkit, an open source toolkit for multi-modal speaker verification and diarization.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
An Embarrassingly Simple Approach for LLM with Strong ASR Capacity
We found that delicate designs are not necessary, while an embarrassingly simple composition of off-the-shelf speech encoder, LLM, and the only trainable linear projector is competent for the ASR task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering
The language model (LM) approach based on acoustic and linguistic prompts, such as VALL-E, has achieved remarkable progress in the field of zero-shot audio generation.
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.
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
In this paper, we propose LauraGPT, a unified GPT model for audio recognition, understanding, and generation.
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.
Towards Universal Speech Discrete Tokens: A Case Study for ASR and TTS
Self-supervised learning (SSL) proficiency in speech-related tasks has driven research into utilizing discrete tokens for speech tasks like recognition and translation, which offer lower storage requirements and great potential to employ natural language processing techniques.
VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching
Although diffusion models in text-to-speech have become a popular choice due to their strong generative ability, the intrinsic complexity of sampling from diffusion models harms their efficiency.
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.
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation
Recently, end-to-end (E2E) automatic speech recognition (ASR) models have made great strides and exhibit excellent performance in general speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
LTCR: Long-Text Chinese Rumor Detection Dataset
False information can spread quickly on social media, negatively influencing the citizens' behaviors and responses to social events.
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
Hierarchical Deep Residual Reasoning for Temporal Moment Localization
Temporal Moment Localization (TML) in untrimmed videos is a challenging task in the field of multimedia, which aims at localizing the start and end points of the activity in the video, described by a sentence query.
Video Super-Resolution via Deep Draft-Ensemble Learning
We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.
Handling Motion Blur in Multi-Frame Super-Resolution
Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR).
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering.
