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 19512000 of 6433 papers

TitleStatusHype
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit0
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information0
Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers0
Progressive Multi-Scale Self-Supervised Learning for Speech Recognition0
Robust Speech Recognition via Large-Scale Weak SupervisionCode8
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-LabelsCode1
LMEC: Learnable Multiplicative Absolute Position Embedding Based Conformer for Speech RecognitionCode0
Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models0
Continual Learning for On-Device Speech Recognition using Disentangled Conformers0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems0
Cross-Modal Mutual Learning for Cued Speech Recognition0
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition0
Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition0
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback0
Preliminary Study on SSCF-derived Polar Coordinate for ASR0
EURO: ESPnet Unsupervised ASR Open-source Toolkit0
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition0
On Word Error Rate Definitions and their Efficient Computation for Multi-Speaker Speech Recognition SystemsCode1
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation0
Better Transcription of UK Supreme Court Hearings0
Evaluating and reducing the distance between synthetic and real speech distributions0
Handling and extracting key entities from customer conversations using Speech recognition and Named Entity recognition0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition0
Bidirectional Representations for Low Resource Spoken Language Understanding0
Multitask Learning for Low Resource Spoken Language Understanding0
Improving Multi-task Learning via Seeking Task-based Flat Regions0
Whose Emotion Matters? Speaking Activity Localisation without Prior KnowledgeCode0
Device Directedness with Contextual Cues for Spoken Dialog Systems0
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction0
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition0
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution0
Towards continually learning new languages0
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale0
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning0
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective0
Exploring WavLM on Speech Enhancement0
A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text CorporaCode1
Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review0
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition0
MelHuBERT: A simplified HuBERT on Mel spectrogramsCode1
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer0
L2 proficiency assessment using self-supervised speech representations0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer0
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches0
Streaming Joint Speech Recognition and Disfluency DetectionCode0
Introducing Semantics into Speech Encoders0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder0
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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