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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4001–4050 of 6433 papers

TitleStatusHype
Self-Attentional Models for Lattice Inputs—0
Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition—0
Self-Attention Transducers for End-to-End Speech Recognition—0
Self-consistent context aware conformer transducer for speech recognition—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model—0
Self-Normalized Importance Sampling for Neural Language Modeling—0
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition—0
Self-reinforcing Unsupervised Matching—0
Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification—0
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition—0
Self-Supervised Learning-Based Source Separation for Meeting Data—0
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition—0
Self-supervised Learning with Speech Modulation Dropout—0
Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks—0
Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning—0
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations—0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Self-Supervised Representations Improve End-to-End Speech Translation—0
Self-supervised representations in speech-based depression detection—0
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition—0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text—0
Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric—0
Self-Supervised Speech Recognition via Local Prior Matching—0
Self-Supervised Speech Representation Learning: A Review—0
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices—0
Self-Teaching Networks—0
Self-Training for End-to-End Speech Recognition—0
Self-Training for End-to-End Speech Translation—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions—0
Semantic Communications for Speech Recognition—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding—0
Semantic Language Model for Tunisian Dialect—0
Semantic parsing of speech using grammars learned with weak supervision—0
Semantic-preserved Communication System for Highly Efficient Speech Transmission—0
Semantic Role Labeling Improves Incremental Parsing—0
Semantic sentence similarity: size does not always matter—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction—0
Semantic-WER: A Unified Metric for the Evaluation of ASR Transcript for End Usability—0
SeMaScore : a new evaluation metric for automatic speech recognition tasks—0
SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge—0
Semi-automatic annotation of the UCU accents speech corpus—0
Semi-Autoregressive Streaming ASR With Label Context—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech—0
Semi-supervised acoustic model training for speech with code-switching—0
Show:102550
← PrevPage 81 of 129Next →
All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified