Speech Recognition
1090 papers with code • 234 benchmarks • 87 datasets
Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
( Image credit: SpecAugment )
Benchmarks
These leaderboards are used to track progress in Speech Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Speech Recognition models and implementationsDatasets
MNIST
LibriSpeech
Speech Commands
Common Voice
MuST-C
Libri-Light
AISHELL-1
LRS2
Subtasks
Latest papers with no code
Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models
We identify subgroups of audio recordings based on combinations of these metadata and compute each subgroup's performance (e. g., Word Error Rate) and the difference in performance (''divergence'') w. r. t the overall population.
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models
Our experiments with recent long-context LLMs on ELITR-Bench highlight a gap between open-source and proprietary models, especially when questions are asked sequentially within a conversation.
LV-CTC: Non-autoregressive ASR with CTC and latent variable models
In this paper, we propose a new model combining CTC and a latent variable model, which is one of the state-of-the-art models in the neural machine translation research field.
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition
Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks.
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus
We present ZAEBUC-Spoken, a multilingual multidialectal Arabic-English speech corpus.
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks.
Extracting Biomedical Entities from Noisy Audio Transcripts
Our dataset offers a comprehensive collection of almost 2, 000 clean and noisy recordings.
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT
Text continues to remain a relevant form of representation for information.
Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models
However, their per-task parameter overhead is considered still high when the number of downstream tasks to adapt for is large.
Privacy-Preserving End-to-End Spoken Language Understanding
Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy.
