SOTAVerified

Speech Recognition

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 )

Papers

Showing 101150 of 6433 papers

TitleStatusHype
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural ConversationCode2
Vakyansh: ASR Toolkit for Low Resource Indic languagesCode2
MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text TranslationCode2
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech EnhancementCode2
BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech RecognitionCode2
Mamba in Speech: Towards an Alternative to Self-AttentionCode2
LiteASR: Efficient Automatic Speech Recognition with Low-Rank ApproximationCode2
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text RecognitionCode2
LightSeq2: Accelerated Training for Transformer-based Models on GPUsCode2
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU LanguagesCode2
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced AttentionCode2
Large Language Models are Strong Audio-Visual Speech Recognition LearnersCode2
FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech CodecCode2
Liquid Structural State-Space ModelsCode2
Fast Transformers with Clustered AttentionCode2
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPTCode2
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface ElectromyographyCode2
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech RecognitionCode2
FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher informationCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
Auto-AVSR: Audio-Visual Speech Recognition with Automatic LabelsCode2
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster PredictionCode2
audino: A Modern Annotation Tool for Audio and SpeechCode2
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
CTC-synchronous Training for Monotonic Attention ModelCode1
Attention model for articulatory features detectionCode1
Cross-Speaker Encoding Network for Multi-Talker Speech RecognitionCode1
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
Attention-Based Models for Speech RecognitionCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Cross Attention Augmented Transducer Networks for Simultaneous TranslationCode1
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech RecognitionCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
A transfer learning based approach for pronunciation scoringCode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic FeaturesCode1
CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian PortugueseCode1
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)Code1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
CopyNE: Better Contextual ASR by Copying Named EntitiesCode1
CoVoST 2 and Massively Multilingual Speech-to-Text TranslationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN MMIPercentage error12.9Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN BMMIPercentage error12.9Unverified
9DNN MPEPercentage error12.9Unverified
10Deep Speech + FSHPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
5Deep Speech 2Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified