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

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Showing 5351–5400 of 6433 papers

TitleStatusHype
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals—0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning—0
Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding—0
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition—0
Sequence-to-sequence models in peer-to-peer learning: A practical application—0
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions—0
Sequence Training and Adaptation of Highway Deep Neural Networks—0
Sequence Transduction with Graph-based Supervision—0
Sequential Editing for Lifelong Training of Speech Recognition Models—0
Sequential End-to-End Intent and Slot Label Classification and Localization—0
Sequential Labeling for Tracking Dynamic Dialog States—0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution—0
Serialized Output Training by Learned Dominance—0
Serialized Output Training for End-to-End Overlapped Speech Recognition—0
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition—0
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants—0
Session-level Language Modeling for Conversational Speech—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
調變頻譜分解之改良於強健性語音辨識(Several Refinements of Modulation Spectrum Factorization for Robust Speech Recognition) [In Chinese]—0
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
室內遠距離語音辨識實驗(Experiments on In-House Far-Field Speech Recognition)—0
使用長短期記憶類神經網路建構中文語音辨識器之研究 (A Study on Mandarin Speech Recognition using Long Short- Term Memory Neural Network)—0
使用生成對抗網路於強健式自動語音辨識的應用(Exploiting Generative Adversarial Network for Robustness Automatic Speech Recognition)—0
Shortcut Learning Susceptibility in Vision Classifiers—0
Short-Term Projects, Long-Term Benefits: Four Student NLP Projects for Low-Resource Languages—0
Short-Term Word-Learning in a Dynamically Changing Environment—0
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition—0
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions—0
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Signal Combination for Language Identification—0
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation—0
Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition—0
Silent Speech Interfaces for Speech Restoration: A Review—0
Silent versus modal multi-speaker speech recognition from ultrasound and video—0
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research—0
Similarity and Content-based Phonetic Self Attention for Speech Recognition—0
Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference—0
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test—0
Simple and Effective Unsupervised Speech Synthesis—0
Simple and Effective Unsupervised Speech Translation—0
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
Simplified End-to-End MMI Training and Voting for ASR—0
Simplified guidelines for the creation of Large Scale Dialectal Arabic Annotations—0
Simplified Self-Attention for Transformer-based End-to-End Speech Recognition—0
Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition—0
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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