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

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Showing 4201–4250 of 6433 papers

TitleStatusHype
Speaker Diarization of Scripted Audiovisual Content—0
Speaker Diarization with Lexical Information—0
Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition—0
Speaker Identification using Speech Recognition—0
Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models—0
Speaker Mask Transformer for Multi-talker Overlapped Speech Recognition—0
Speaker Recognition in Bengali Language from Nonlinear Features—0
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition—0
Speaker Selective Beamformer with Keyword Mask Estimation—0
Speaker Separation Using Speaker Inventories and Estimated Speech—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Speaker-Targeted Audio-Visual Models for Speech Recognition in Cocktail-Party Environments—0
Speak & Improve Challenge 2025: Tasks and Baseline Systems—0
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback—0
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning—0
SpecAugment on Large Scale Datasets—0
Spectral decomposition method of dialog state tracking via collective matrix factorization—0
Spectral Dependency Parsing with Latent Variables—0
Spectral feature mapping with mimic loss for robust speech recognition—0
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech—0
Spectral modification for recognition of children’s speech undermismatched conditions—0
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition—0
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models—0
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech—0
Speech and language technologies for the automatic monitoring and training of cognitive functions—0
Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator—0
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks—0
Speech Aware Dialog System Technology Challenge (DSTC11)—0
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering—0
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language—0
Speech Corpus Spoken by Young-old, Old-old and Oldest-old Japanese—0
Speech Diarization and ASR with GMM—0
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks—0
Speech-Enabled Computer-Aided Translation: A Satisfaction Survey with Post-Editor Trainees—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
Speech Enhancement Modeling Towards Robust Speech Recognition System—0
Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks—0
Speech Enhancement Using Pitch Detection Approach For Noisy Environment—0
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention—0
Speech enhancement with frequency domain auto-regressive modeling—0
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization—0
Speech inpainting: Context-based speech synthesis guided by video—0
Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?—0
SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data—0
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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