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 51–100 of 6433 papers

TitleStatusHype
AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning—0
Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems—0
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
ViCocktail: Automated Multi-Modal Data Collection for Vietnamese Audio-Visual Speech Recognition—0
LLM-based phoneme-to-grapheme for phoneme-based speech recognition—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts—0
Structured State Space Model Dynamics and Parametrization for Spiking Neural NetworksCode0
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR—0
MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition—0
A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation—0
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning—0
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss—0
Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data—0
TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge—0
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation—0
DNCASR: End-to-End Training for Speaker-Attributed ASR—0
Riemannian Time Warping: Multiple Sequence Alignment in Curved Spaces—0
Cocktail-Party Audio-Visual Speech Recognition—0
Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric—0
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric SpeechCode0
Analyzing the Importance of Blank for CTC-Based Knowledge Distillation—0
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing—0
Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody—0
What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-trainingCode0
GigaAM: Efficient Self-Supervised Learner for Speech RecognitionCode4
No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction—0
DYNAC: Dynamic Vocabulary based Non-Autoregressive Contextualization for Speech Recognition—0
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems—0
Causal Structure Discovery for Error Diagnostics of Children's ASR—0
Towards Temporally Explainable Dysarthric Speech Clarity AssessmentCode0
MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition—0
Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit PoetryCode0
Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization—0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR—0
Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC—0
Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach—0
Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction—0
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation SystemCode0
Prompting Whisper for Improved Verbatim Transcription and End-to-end Miscue Detection—0
Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation—0
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding—0
Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis—0
Evaluation of LLMs in Speech is Often Flawed: Test Set Contamination in Large Language Models for Speech Recognition—0
CNVSRC 2024: The Second Chinese Continuous Visual Speech Recognition Challenge—0
Leveraging Large Language Models in Visual Speech Recognition: Model Scaling, Context-Aware Decoding, and Iterative Polishing—0
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation—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