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 3401–3450 of 6433 papers

TitleStatusHype
Novel Loss-Enhanced Universal Adversarial Patches for Sustainable Speaker Privacy—0
N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition—0
NSYSU-MITLab團隊於福爾摩沙語音辨識競賽2020之語音辨識系統 (NSYSU-MITLab Speech Recognition System for Formosa Speech Recognition Challenge 2020)—0
NTP : A Neural Network Topology Profiler—0
NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge—0
NTT speaker diarization system for CHiME-7: multi-domain, multi-microphone End-to-end and vector clustering diarization—0
NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025—0
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity—0
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference—0
NUTS, NARS, and Speech—0
NUVA: A Naming Utterance Verifier for Aphasia Treatment—0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022—0
O-1: Self-training with Oracle and 1-best Hypothesis—0
OAVA: the open audio-visual archives aggregator—0
OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline—0
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality—0
Off-topic Response Detection for Spontaneous Spoken English Assessment—0
OkwuGbé: End-to-End Speech Recognition for Fon and Igbo—0
OLR 2021 Challenge: Datasets, Rules and Baselines—0
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet—0
On Addressing Practical Challenges for RNN-Transducer—0
On a novel training algorithm for sequence-to-sequence predictive recurrent networks—0
On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
On combining features for single-channel robust speech recognition in reverberant environments—0
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms—0
On-Device detection of sentence completion for voice assistants with low-memory footprint—0
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech—0
On-Device Speaker Anonymization of Acoustic Embeddings for ASR based onFlexible Location Gradient Reversal Layer—0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition—0
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement—0
One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble—0
One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition—0
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments—0
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers—0
One-To-Many Multilingual End-to-end Speech Translation—0
On Generalization and Regularization in Deep Learning—0
On Knowledge Distillation for Direct Speech Translation—0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions—0
On Language Model Integration for RNN Transducer based Speech Recognition—0
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models—0
Online Automatic Speech Recognition with Listen, Attend and Spell Model—0
Online Continual Learning of End-to-End Speech Recognition Models—0
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture—0
Online Model Compression for Federated Learning with Large Models—0
Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification—0
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation—0
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer—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