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 18011850 of 6433 papers

TitleStatusHype
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms0
Trustera: A Live Conversation Redaction System0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model0
A large-scale multimodal dataset of human speech recognition0
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken SentencesCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability ScoringCode1
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models0
Improving Accented Speech Recognition with Multi-Domain Training0
Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy0
I3D: Transformer architectures with input-dependent dynamic depth for speech recognitionCode0
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model0
Improving the Intent Classification accuracy in Noisy Environment0
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative StudyCode0
The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge0
Stabilizing Transformer Training by Preventing Attention Entropy CollapseCode2
Transcription free filler word detection with Neural semi-CRFsCode0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings0
An Overview on Language Models: Recent Developments and Outlook0
Unsupervised Language agnostic WER Standardization0
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural NetworksCode0
TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker EmbeddingsCode1
wav2vec and its current potential to Automatic Speech Recognition in German for the usage in Digital History: A comparative assessment of available ASR-technologies for the use in cultural heritage contexts0
Calibrating Transformers via Sparse Gaussian ProcessesCode1
End-to-End Speech Recognition: A Survey0
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis0
SottoVoce: An Ultrasound Imaging-Based Silent Speech Interaction Using Deep Neural Networks0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages0
Leveraging Large Text Corpora for End-to-End Speech Summarization0
LiteG2P: A fast, light and high accuracy model for grapheme-to-phoneme conversion0
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space0
MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text TranslationCode2
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition0
Practice of the conformer enhanced AUDIO-VISUAL HUBERT on Mandarin and English0
Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech RecognitionCode0
Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition0
A Token-Wise Beam Search Algorithm for RNN-T0
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit0
Diagonal State Space Augmented Transformers for Speech Recognition0
Diacritic Recognition Performance in Arabic ASR0
Explanations for Automatic Speech Recognition0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model0
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and UnderstandingCode1
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition0
Multimodal Speech Recognition for Language-Guided Embodied AgentsCode0
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator0
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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