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 451500 of 6433 papers

TitleStatusHype
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate UsersCode1
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from SpeechCode1
A Toolbox for Construction and Analysis of Speech DatasetsCode1
RNN Transducer Models For Spoken Language UnderstandingCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible InputsCode1
Librispeech Transducer Model with Internal Language Model Prior CorrectionCode1
Learning to Rank Microphones for Distant Speech RecognitionCode1
ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of KaldiCode1
Keyword Transformer: A Self-Attention Model for Keyword SpottingCode1
Multilingual and code-switching ASR challenges for low resource Indian languagesCode1
Integer-only Zero-shot Quantization for Efficient Speech RecognitionCode1
MediaSpeech: Multilanguage ASR Benchmark and DatasetCode1
Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arraysCode1
Leveraging pre-trained representations to improve access to untranscribed speech from endangered languagesCode1
Radically Old Way of Computing Spectra: Applications in End-to-End ASRCode1
Fast Development of ASR in African Languages using Self Supervised Speech Representation LearningCode1
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research ChallengesCode1
WaveGuard: Understanding and Mitigating Audio Adversarial ExamplesCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
Transformer Language Models with LSTM-based Cross-utterance Information RepresentationCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Dompteur: Taming Audio Adversarial ExamplesCode1
BembaSpeech: A Speech Recognition Corpus for the Bemba LanguageCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled DataCode1
Learning Efficient Representations for Keyword Spotting with Triplet LossCode1
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and InterpretationCode1
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear MapsCode1
Scalable Optical Learning OperatorCode1
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery DetectionCode1
AV Taris: Online Audio-Visual Speech RecognitionCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech RecognitionCode1
SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video StreamsCode1
metaCAT: A Metadata-based Task-oriented Chatbot Annotation ToolCode1
End-to-End Automatic Speech Recognition for GujaratiCode1
Deep Discriminative Feature Learning for Accent RecognitionCode1
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network LatencyCode1
Learn an Effective Lip Reading Model without PainsCode1
Text Augmentation for Language Models in High Error Recognition ScenarioCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Nanopore Base Calling on the EdgeCode1
Improving RNN Transducer Based ASR with Auxiliary TasksCode1
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arraysCode1
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASRCode1
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech TranslationCode1
Adapting Pretrained Transformer to Lattices for Spoken Language UnderstandingCode1
Punctuation Restoration using Transformer Models for High-and Low-Resource LanguagesCode1
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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