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 251300 of 6433 papers

TitleStatusHype
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language RecognitionCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
Continuous speech separation: dataset and analysisCode1
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
Enhancing Monotonic Multihead Attention for Streaming ASRCode1
ESB: A Benchmark For Multi-Domain End-to-End Speech RecognitionCode1
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and RecognitionCode1
AVATAR: Unconstrained Audiovisual Speech RecognitionCode1
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNetCode1
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling InsightsCode1
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based AdaptationCode1
Espresso: A Fast End-to-end Neural Speech Recognition ToolkitCode1
Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task LearningCode1
Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-AttentionCode1
End-to-end Named Entity Recognition from English SpeechCode1
Automatic Disfluency Detection from Untranscribed SpeechCode1
Automatic Speech Recognition Benchmark for Air-Traffic CommunicationsCode1
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech TranslationCode1
AutoDiCE: Fully Automated Distributed CNN Inference at the EdgeCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognitionCode1
EnCodecMAE: Leveraging neural codecs for universal audio representation learningCode1
End-to-End Automatic Speech Recognition for GujaratiCode1
End-to-End Speech Recognition and Disfluency RemovalCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
Emotionless: Privacy-Preserving Speech Analysis for Voice AssistantsCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
Emotion Recognition in Audio and Video Using Deep Neural NetworksCode1
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
Accented Speech Recognition With Accent-specific CodebooksCode1
Automatic Speech Recognition for Speech Assessment of Persian Preschool ChildrenCode1
Attention model for articulatory features detectionCode1
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation LearningCode1
Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition SystemCode1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
FAST-RIR: Fast neural diffuse room impulse response generatorCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
Goodness of Pronunciation Pipelines for OOV ProblemCode1
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Earnings-22: A Practical Benchmark for Accents in the WildCode1
A transfer learning based approach for pronunciation scoringCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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 BMMIPercentage error12.9Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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