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 35513600 of 6433 papers

TitleStatusHype
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept0
EAT: Enhanced ASR-TTS for Self-supervised Speech RecognitionCode0
Experiments of ASR-based mispronunciation detection for children and adult English learners0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures0
NeMo Inverse Text Normalization: From Development To ProductionCode0
Innovative Bert-based Reranking Language Models for Speech Recognition0
Non-autoregressive Transformer-based End-to-end ASR using BERT0
On Architectures and Training for Raw Waveform Feature Extraction in ASR0
Accented Speech Recognition Inspired by Human Perception0
Language model fusion for streaming end to end speech recognition0
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 20200
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset0
Layer Reduction: Accelerating Conformer-Based Self-Supervised Model via Layer Consistency0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems0
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation0
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization0
Pushing the Limits of Non-Autoregressive Speech Recognition0
Capturing Multi-Resolution Context by Dilated Self-Attention0
Optimal Transport-based Adaptation in Dysarthric Speech Tasks0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition0
AI4D -- African Language ProgramCode0
Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions0
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios0
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoringCode0
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models0
Non-autoregressive Mandarin-English Code-switching Speech Recognition0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model0
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding0
End-to-End Speaker-Attributed ASR with Transformer0
Streaming Multi-talker Speech Recognition with Joint Speaker Identification0
Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency0
SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognitionCode0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR0
Talk, Don't Write: A Study of Direct Speech-Based Image Retrieval0
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network0
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion0
Towards Lifelong Learning of End-to-end ASR0
TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling0
Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties0
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech RecognitionCode0
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRCode0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition0
HMM-Free Encoder Pre-Training for Streaming RNN Transducer0
Disfluency Correction using Unsupervised and Semi-supervised Learning0
Show:102550
← PrevPage 72 of 129Next →

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