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 37013750 of 6433 papers

TitleStatusHype
DNN-Based Semantic Model for Rescoring N-best Speech Recognition List0
Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation0
Adapting Pretrained Transformer to Lattices for Spoken Language UnderstandingCode1
ELITR: European Live Translator0
Punctuation Restoration using Transformer Models for High-and Low-Resource LanguagesCode1
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others0
May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance0
Simultaneous Translation0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training0
Direct Segmentation Models for Streaming Speech TranslationCode0
Effectively pretraining a speech translation decoder with Machine Translation data0
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced LanguagesCode0
Streaming Simultaneous Speech Translation with Augmented Memory Transformer0
Joint Masked CPC and CTC Training for ASRCode1
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization0
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition0
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework0
Semi-Supervised Speech Recognition via Graph-based Temporal Classification0
May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition0
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices0
Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input0
Fusion Models for Improved Visual Captioning0
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition0
CASS-NAT: CTC Alignment-based Single Step Non-autoregressive Transformer for Speech Recognition0
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition0
End-to-End Far-Field Speech Recognition with Unified Dereverberation and Beamforming0
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications0
Cascaded encoders for unifying streaming and non-streaming ASR0
Multitask Training with Text Data for End-to-End Speech Recognition0
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation LearningCode1
Emotion recognition by fusing time synchronous and time asynchronous representations0
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer0
Improved Mask-CTC for Non-Autoregressive End-to-End ASR0
Lip Graph Assisted Audio-Visual Speech Recognition Using Bidirectional Synchronous Fusion0
Probing Acoustic Representations for Phonetic PropertiesCode0
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task LearningCode0
Two-stage Textual Knowledge Distillation for End-to-End Spoken Language UnderstandingCode0
Auxiliary Sequence Labeling Tasks for Disfluency Detection0
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment0
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition0
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer0
Transformer-based End-to-End Speech Recognition with Local Dense Synthesizer AttentionCode0
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework0
MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation0
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data0
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
How Phonotactics Affect Multilingual and Zero-shot ASR PerformanceCode0
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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