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 201250 of 6433 papers

TitleStatusHype
Deep Contextualized Acoustic Representations For Semi-Supervised Speech RecognitionCode1
Deep Learning Enabled Semantic Communications with Speech Recognition and SynthesisCode1
Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)Code1
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognitionCode1
Adaptation of Whisper models to child speech recognitionCode1
Framework for Curating Speech Datasets and Evaluating ASR Systems: A Case Study for PolishCode1
Computer-Generated Music for Tabletop Role-Playing GamesCode1
Discriminative Multi-modality Speech RecognitionCode1
Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTCCode1
Adapting End-to-End Speech Recognition for Readable SubtitlesCode1
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural NetworksCode1
Compiling ONNX Neural Network Models Using MLIRCode1
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition BaselineCode1
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arraysCode1
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
DOVER: A Method for Combining Diarization OutputsCode1
Do VSR Models Generalize Beyond LRS3?Code1
Adapting Pretrained Transformer to Lattices for Spoken Language UnderstandingCode1
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question AnsweringCode1
DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic ModelCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMICode1
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation LearningCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
Emotion Recognition in Audio and Video Using Deep Neural NetworksCode1
EnCodecMAE: Leveraging neural codecs for universal audio representation learningCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic FeaturesCode1
End-to-End Speech Recognition and Disfluency RemovalCode1
Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based AdaptationCode1
Enhancing Monotonic Multihead Attention for Streaming ASRCode1
Espresso: A Fast End-to-end Neural Speech Recognition ToolkitCode1
Evaluating Speech Synthesis by Training Recognizers on Synthetic SpeechCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
Evolutionary Prompt Design for LLM-Based Post-ASR Error CorrectionCode1
CopyNE: Better Contextual ASR by Copying Named EntitiesCode1
A Cross-Modal Approach to Silent Speech with LLM-Enhanced RecognitionCode1
Combining Frame-Synchronous and Label-Synchronous Systems for Speech RecognitionCode1
Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation LearningCode1
Common Voice: A Massively-Multilingual Speech CorpusCode1
Radically Old Way of Computing Spectra: Applications in End-to-End ASRCode1
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing ModelsCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact CentersCode1
CL-MASR: A Continual Learning Benchmark for Multilingual ASRCode1
CLSRIL-23: Cross Lingual Speech Representations for Indic 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