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 24512500 of 6433 papers

TitleStatusHype
Factual Consistency Oriented Speech Recognition0
Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation0
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition0
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition0
Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech0
Chaotic Variational Auto encoder-based Adversarial Machine Learning0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans0
FARMI: A FrAmework for Recording Multi-Modal Interactions0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction0
Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community0
A Novel Self-training Approach for Low-resource Speech Recognition0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging0
Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models0
Fast and Accurate OOV Decoder on High-Level Features0
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition0
Fast and Accurate Reordering with ITG Transition RNN0
Fast and parallel decoding for transducer0
Fast and Robust Neural Network Joint Models for Statistical Machine Translation0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition0
Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation0
Fast Bootstrapping of Grapheme to Phoneme System for Under-resourced Languages - Application to the Iban Language0
Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers0
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition0
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation0
A Quantitative Insight into the Impact of Translation on Readability0
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT0
Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition0
Faster Speech-LLaMA Inference with Multi-token Prediction0
Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway0
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training0
Fast Labeling and Transcription with the Speechalyzer Toolkit0
Enriching ASR Lattices with POS Tags for Dependency Parsing0
Fast Node Embeddings: Learning Ego-Centric Representations0
Fast offline Transformer-based end-to-end automatic speech recognition for real-world applications0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation0
A Novel Method for improving accuracy in neural network by reinstating traditional back propagation technique0
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm0
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces0
CHISPA on the GO: A mobile Chinese-Spanish translation service for travellers in trouble0
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper0
Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining0
Enhancing Whisper's Accuracy and Speed for Indian Languages through Prompt-Tuning and Tokenization0
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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