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 1701–1750 of 6433 papers

TitleStatusHype
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition—0
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition—0
Correction Focused Language Model Training for Speech Recognition—0
Generative error correction for code-switching speech recognition using large language models—0
Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition—0
Multi-stage Large Language Model Correction for Speech Recognition—0
Optimized Tokenization for Transcribed Error Correction—0
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model—0
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization—0
End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder and Input Feature Analysis—0
Large Vocabulary Spontaneous Speech Recognition for Tigrigna—0
Homophone Disambiguation Reveals Patterns of Context Mixing in Speech TransformersCode0
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring—0
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation—0
On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
Adapting the adapters for code-switching in multilingual ASRCode0
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation—0
Discriminative Speech Recognition Rescoring with Pre-trained Language Models—0
Acoustic Model Fusion for End-to-end Speech Recognition—0
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond—0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis—0
Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language UnderstandingCode0
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correctionCode0
End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition—0
Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition—0
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model—0
Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition EncoderCode0
A privacy-preserving method using secret key for convolutional neural network-based speech classification—0
The North System for Formosa Speech Recognition Challenge 2023—0
DecoderLens: Layerwise Interpretation of Encoder-Decoder TransformersCode0
Neural Language Model Pruning for Automatic Speech Recognition—0
Challenges and Insights: Exploring 3D Spatial Features and Complex Networks on the MISP Dataset—0
An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples—0
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions—0
ResidualTransformer: Residual Low-Rank Learning with Weight-Sharing for Transformer Layers—0
One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
SLM: Bridge the thin gap between speech and text foundation models—0
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR—0
AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm—0
Enhancing Code-switching Speech Recognition with Interactive Language Biases—0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning—0
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition—0
Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization—0
PP-MeT: a Real-world Personalized Prompt based Meeting Transcription System—0
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR—0
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting—0
Show:102550
← PrevPage 35 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