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 38513900 of 6433 papers

TitleStatusHype
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech0
Residual Convolutional CTC Networks for Automatic Speech Recognition0
Residual Energy-Based Models for End-to-End Speech Recognition0
Residual Language Model for End-to-end Speech Recognition0
ResidualTransformer: Residual Low-Rank Learning with Weight-Sharing for Transformer Layers0
Resilience of Large Language Models for Noisy Instructions0
Resolution limits on visual speech recognition0
Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical System for Punctuation Restoration0
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion0
Resource-Constrained Federated Learning with Heterogeneous Labels and Models0
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion0
Resource Evaluation for Usable Speech Interfaces: Utilizing Human-Human Dialogue0
Response Generation Based on Hierarchical Semantic Structure with POMDP Re-ranking for Conversational Dialogue Systems0
Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition0
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding0
Rethinking Full Connectivity in Recurrent Neural Networks0
Rethinking Mamba in Speech Processing by Self-Supervised Models0
Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance0
Rethinking Speech Recognition with A Multimodal Perspective via Acoustic and Semantic Cooperative Decoding0
Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors0
Retrieval-Augmented Speech Recognition Approach for Domain Challenges0
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction0
Retrieval Term Prediction Using Deep Belief Networks0
Retrieval Term Prediction Using Deep Learning Methods0
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs0
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition0
Reverb: Open-Source ASR and Diarization from Rev0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example0
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement0
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration0
Revising the annotation of a Broadcast News corpus: a linguistic approach0
Revisiting Acoustic Features for Robust ASR0
Revisiting End-to-End Speech-to-Text Translation From Scratch0
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective0
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems0
Revisiting the Case for Explicit Syntactic Information in Language Models0
Revisiting the Entropy Semiring for Neural Speech Recognition0
Revisiting Word Neighborhoods for Speech Recognition0
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation0
Reward Augmented Maximum Likelihood for Neural Structured Prediction0
r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation0
Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling0
Riemannian Time Warping: Multiple Sequence Alignment in Curved Spaces0
RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets0
RNNFast: An Accelerator for Recurrent Neural Networks Using Domain Wall Memory0
RNN-T For Latency Controlled ASR With Improved Beam Search0
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions0
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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