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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 6433 papers

TitleStatusHype
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
CNN architecture extraction on edge GPU—0
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering—0
Locality enhanced dynamic biasing and sampling strategies for contextual ASR—0
Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study—0
Consistency Based Unsupervised Self-training For ASR Personalisation—0
Keep Decoding Parallel with Effective Knowledge Distillation from Language Models to End-to-end Speech Recognisers—0
Using Large Language Model for End-to-End Chinese ASR and NER—0
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free MetricCode1
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search—0
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition—0
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition—0
Efficient Training for Multilingual Visual Speech Recognition: Pre-training with Discretized Visual Speech RepresentationCode1
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition—0
On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement—0
Two-pass Endpoint Detection for Speech Recognition—0
Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization—0
Improving ASR Contextual Biasing with Guided Attention—0
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective—0
NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription—0
SeMaScore : a new evaluation metric for automatic speech recognition tasks—0
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image ModelsCode0
Cascaded Cross-Modal Transformer for Audio-Textual ClassificationCode0
Promptformer: Prompted Conformer Transducer for ASR—0
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization—0
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
Dynamic Behaviour of Connectionist Speech Recognition with Strong Latency Constraints—0
End to end Hindi to English speech conversion using Bark, mBART and a finetuned XLSR Wav2Vec2—0
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction—0
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?—0
Towards Online Continuous Sign Language Recognition and Translation—0
Continuously Learning New Words in Automatic Speech Recognition—0
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR—0
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings—0
Cross-Speaker Encoding Network for Multi-Talker Speech RecognitionCode1
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators—0
Exploratory Evaluation of Speech Content Masking—0
The NPU-ASLP-LiAuto System Description for Visual Speech Recognition in CNVSRC 2023Code1
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge—0
DiarizationLM: Speaker Diarization Post-Processing with Large Language ModelsCode3
MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition—0
Part-of-Speech Tagger for Bodo Language using Deep Learning approach—0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model—0
Nonlinear functional regression by functional deep neural network with kernel embedding—0
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks—0
Show:102550
← PrevPage 22 of 129Next →
All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified