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 28512900 of 6433 papers

TitleStatusHype
Scaling ASR Improves Zero and Few Shot Learning0
ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User InterfacesCode0
Joint Neural AEC and Beamforming with Double-Talk Detection0
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition0
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework0
Sequential Randomized Smoothing for Adversarially Robust Speech RecognitionCode0
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition0
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models0
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition0
Context-Aware Transformer Transducer for Speech Recognition0
Conformer-based Hybrid ASR System for Switchboard Dataset0
Speech recognition for air traffic control via feature learning and end-to-end training0
MT3: Multi-Task Multitrack Music TranscriptionCode1
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding0
Voice Conversion Can Improve ASR in Very Low-Resource Settings0
Sequence-to-Sequence Modeling for Action Identification at High Temporal ResolutionCode0
STC speaker recognition systems for the NIST SRE 20210
Recent Advances in End-to-End Automatic Speech Recognition0
Cross-lingual Transfer for Speech Processing using Acoustic Language SimilarityCode0
Orthographic Transliteration for Kabyle Speech RecognitionCode0
Indic Languages Automatic Speech Recognition using Meta-Learning Approach0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English0
Comparing Grammatical Theories of Code-Mixing0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants0
Intrinsic evaluation of language models for code-switchingCode0
Cross Attention Augmented Transducer Networks for Simultaneous TranslationCode1
Voice Query Auto Completion0
Comprehensive Punctuation Restoration for English and Polish0
SNRi Target Training for Joint Speech Enhancement and Recognition0
A transfer learning based approach for pronunciation scoringCode1
Sequence Transduction with Graph-based Supervision0
Evaluating robustness of You Only Hear Once(YOHO) Algorithm on noisy audios in the VOICe DatasetCode0
Revisiting joint decoding based multi-talker speech recognition with DNN acoustic modelCode1
Revealing and Protecting Labels in Distributed TrainingCode0
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks0
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Pseudo-Labeling for Massively Multilingual Speech Recognition0
Cross-attention conformer for context modeling in speech enhancement for ASR0
Speaker conditioning of acoustic models using affine transformation for multi-speaker speech recognition0
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition0
Continuous Speech Separation with Recurrent Selective Attention Network0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction0
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions0
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech ProcessingCode3
ViDA-MAN: Visual Dialog with Digital Humans0
Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition0
Asynchronous Decentralized Distributed Training of Acoustic Models0
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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 BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage 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
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