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 30513100 of 6433 papers

TitleStatusHype
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data0
Improving the Robustness of Speech Translation0
Improving the Training Recipe for a Robust Conformer-based Hybrid Model0
Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer0
Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community0
Character-Aware Attention-Based End-to-End Speech Recognition0
FARMI: A FrAmework for Recording Multi-Modal Interactions0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction0
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans0
Chaotic Variational Auto encoder-based Adversarial Machine Learning0
Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text0
Improving Zero-Shot Chinese-English Code-Switching ASR with kNN-CTC and Gated Monolingual Datastores0
Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech0
IMS-Speech: A Speech to Text Tool0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task0
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition0
Inappropriate Pause Detection In Dysarthric Speech Using Large-Scale Speech Recognition0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation0
Inclusivity of AI Speech in Healthcare: A Decade Look Back0
In-context Language Learning for Endangered Languages in Speech Recognition0
In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties0
CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi0
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis0
A Probabilistic Approach for Confidence Scoring in Speech Recognition0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise0
Incorporating Language Level Information into Acoustic Models0
Incorporating Side Information into Recurrent Neural Network Language Models0
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement0
Increasing the Accessibility of Time-Aligned Speech Corpora with Spokes Mix0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models0
Incremental Adaptation Strategies for Neural Network Language Models0
Incremental Derivations in CCG0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation0
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device0
Factual Consistency Oriented Speech Recognition0
Incremental LSTM-based Dialog State Tracker0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech0
Incremental Neo-Davidsonian semantic construction for TAG0
RNN based Incremental Online Spoken Language Understanding0
Incremental Predictive Parsing with TurboParser0
A privacy-preserving method using secret key for convolutional neural network-based speech classification0
Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems0
Challenging the Boundaries of Speech Recognition: The MALACH Corpus0
Independent language modeling architecture for end-to-end ASR0
Factored Language Model based on Recurrent Neural Network0
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations0
Show:102550
← PrevPage 62 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