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 14511500 of 6433 papers

TitleStatusHype
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model0
Contextual Language Model Adaptation for Conversational Agents0
Contextual Metric Meta-Evaluation by Measuring Local Metric Accuracy0
CobaltF: A Fluent Metric for MT Evaluation0
Contextual RNN-T For Open Domain ASR0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems0
A review of on-device fully neural end-to-end automatic speech recognition algorithms0
Contextual-Utterance Training for Automatic Speech Recognition0
Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework0
Continual Learning for End-to-End ASR by Averaging Domain Experts0
Continual Learning for On-Device Speech Recognition using Disentangled Conformers0
Continual Learning in Machine Speech Chain Using Gradient Episodic Memory0
Coarse-To-Fine And Cross-Lingual ASR Transfer0
Towards continually learning new languages0
CoALT: A Software for Comparing Automatic Labelling Tools0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search0
Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM0
Continuous Learning for Children's ASR: Overcoming Catastrophic Forgetting with Elastic Weight Consolidation and Synaptic Intelligence0
Continuously Learning New Words in Automatic Speech Recognition0
CNVSRC 2024: The Second Chinese Continuous Visual Speech Recognition Challenge0
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning0
Continuous multilinguality with language vectors0
Continuous Pseudo-Labeling from the Start0
CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge0
Continuous Soft Pseudo-Labeling in ASR0
Continuous Speech Recognition using EEG and Video0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search0
Continuous Speech Separation with Ad Hoc Microphone Arrays0
Continuous Speech Separation with Recurrent Selective Attention Network0
Contour-based 3d tongue motion visualization using ultrasound image sequences0
Contour-based Hand Pose Recognition for Sign Language Recognition0
A Joint Model of Orthography and Morphological Segmentation0
Learning Video Representations using Contrastive Bidirectional Transformer0
A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network0
Contrastive Semi-supervised Learning for ASR0
Contrastive Siamese Network for Semi-supervised Speech Recognition0
Contribution \`a l'\'etude de la variabilit\'e de la voix des personnes \^ag\'ees en reconnaissance automatique de la parole (Contribution to the study of elderly people's voice variability in automatic speech recognition) [in French]0
Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection0
Controlled Ascent: Imbuing Statistical MT with Linguistic Knowledge0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents0
Conventional Orthography for Dialectal Arabic0
Conversational AI: The Science Behind the Alexa Prize0
Conversational Speech Recognition By Learning Conversation-level Characteristics0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments0
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition0
Conversational Speech Recognition Needs Data? Experiments with Austrian German0
Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using Natural Language Processing0
Converting Continuous-Space Language Models into N-Gram Language Models for Statistical Machine Translation0
CNN architecture extraction on edge GPU0
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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