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Found 20 papers, 12 papers with code
Findings of the IWSLT 2024 Evaluation Campaign
This paper reports on the shared tasks organized by the 21st IWSLT Conference.
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Ranked #2 on
Speech Recognition
on AISHELL-1
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Revisiting Interpolation Augmentation for Speech-to-Text Generation
Speech-to-text (S2T) generation systems frequently face challenges in low-resource scenarios, primarily due to the lack of extensive labeled datasets.
Speech Translation with Large Language Models: An Industrial Practice
Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM.
Bridging the Gaps of Both Modality and Language: Synchronous Bilingual CTC for Speech Translation and Speech Recognition
In this study, we present synchronous bilingual Connectionist Temporal Classification (CTC), an innovative framework that leverages dual CTC to bridge the gaps of both modality and language in the speech translation (ST) task.
Recent Advances in Direct Speech-to-text Translation
Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly.
MOSPC: MOS Prediction Based on Pairwise Comparison
And our framework also surpasses the strong baseline in ranking accuracy on each fine-grained segment.
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
CTC-based Non-autoregressive Speech Translation
Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency.
M3ST: Mix at Three Levels for Speech Translation
How to solve the data scarcity problem for end-to-end speech-to-text translation (ST)?
Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation
Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently.
Learning When to Translate for Streaming Speech
Given a usually long speech sequence, we develop an efficient monotonic segmentation module inside an encoder-decoder model to accumulate acoustic information incrementally and detect proper speech unit boundaries for the input in speech translation task.
The Volctrans Neural Speech Translation System for IWSLT 2021
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
NeurST: Neural Speech Translation Toolkit
NeurST is an open-source toolkit for neural speech translation.
Ranked #1 on
Speech-to-Text Translation
on libri-trans
"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
Can we build a system to fully utilize signals in a parallel ST corpus?
Consecutive Decoding for Speech-to-text Translation
The key idea is to generate source transcript and target translation text with a single decoder.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
In this paper, we introduce the Chinese corpus from CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation.
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese.
Chinese Named Entity Recognition
named-entity-recognition
+2
Semi-Supervised Disfluency Detection
While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity.
