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 4651–4700 of 6433 papers

TitleStatusHype
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model—0
Noisy-target Training: A Training Strategy for DNN-based Speech Enhancement without Clean Speech—0
Noisy Training Improves E2E ASR for the Edge—0
Non-Autoregressive Chinese ASR Error Correction with Phonological Training—0
Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding—0
Non-autoregressive Mandarin-English Code-switching Speech Recognition—0
Non-autoregressive real-time Accent Conversion model with voice cloning—0
Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input—0
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition—0
Non-autoregressive Transformer-based End-to-end ASR using BERT—0
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition—0
Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures—0
Nonlinear functional regression by functional deep neural network with kernel embedding—0
Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion—0
Non-native children speech recognition through transfer learning—0
Non-Parallel Voice Conversion for ASR Augmentation—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
Nonparametric Bayesian Semi-supervised Word Segmentation—0
Non-verbal information in spontaneous speech -- towards a new framework of analysis—0
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech—0
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation—0
Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection—0
Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions—0
"Notic My Speech" -- Blending Speech Patterns With Multimedia—0
NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription—0
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
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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