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 1001–1050 of 6433 papers

TitleStatusHype
Exploration of Adapter for Noise Robust Automatic Speech Recognition—0
Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance GapsCode0
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models—0
An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement—0
ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters—0
Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech ProcessingCode3
Efficient data selection employing Semantic Similarity-based Graph Structures for model training—0
HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced AttentionCode2
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR—0
Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition—0
Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions—0
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification—0
How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena—0
Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading—0
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru—0
UniEnc-CASSNAT: An Encoder-only Non-autoregressive ASR for Speech SSL Models—0
Listening to Multi-talker Conversations: Modular and End-to-end Perspectives—0
Syllable based DNN-HMM Cantonese Speech to Text System—0
An Embarrassingly Simple Approach for LLM with Strong ASR CapacityCode2
Careless Whisper: Speech-to-Text Hallucination HarmsCode0
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese—0
SALAD: Smart AI Language Assistant Daily—0
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models—0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine ExtractionCode0
Self-consistent context aware conformer transducer for speech recognition—0
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech RecognitionCode1
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural ConversationCode2
Progressive unsupervised domain adaptation for ASR using ensemble models and multi-stage training—0
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASRCode1
Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical System for Punctuation Restoration—0
A Comprehensive Study of the Current State-of-the-Art in Nepali Automatic Speech Recognition Systems—0
Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens—0
Digits micro-model for accurate and secure transactions—0
Whispering in Norwegian: Navigating Orthographic and Dialectic Challenges—0
Streaming Sequence Transduction through Dynamic CompressionCode0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents—0
Introduction to speech recognition—0
Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases—0
Computation and Parameter Efficient Multi-Modal Fusion Transformer for Cued Speech Recognition—0
Exploring the limits of decoder-only models trained on public speech recognition corpora—0
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition—0
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer—0
Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition—0
On Speaker Attribution with SURT—0
Towards Event Extraction from Speech with Contextual CluesCode0
Comparison of parameters of vowel sounds of russian and english languages—0
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline—0
TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down FusionCode1
Show:102550
← PrevPage 21 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