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

Showing 43514400 of 6433 papers

TitleStatusHype
Minimally Supervised Number Normalization0
Minimally Supervised Written-to-Spoken Text Normalization0
Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator0
Minimum Bayes Risk based Answer Re-ranking for Question Answering0
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition0
Minimum Latency Training of Sequence Transducers for Streaming End-to-End Speech Recognition0
Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR0
Minimum Translation Modeling with Recurrent Neural Networks0
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition0
Mining linguistic tone patterns with symbolic representation0
Mining Search Query Logs for Spoken Language Understanding0
Minuteman: Machine and Human Joining Forces in Meeting Summarization0
MirasVoice: A bilingual (English-Persian) speech corpus0
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition0
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification0
Mitigating Noisy Inputs for Question Answering0
Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model0
MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge0
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition0
Mixed Precision of Quantization of Transformer Language Models for Speech Recognition0
Mixing Multiple Translation Models in Statistical Machine Translation0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems0
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition0
Mixture Encoder for Joint Speech Separation and Recognition0
Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription0
Mixture-of-Expert Conformer for Streaming Multilingual ASR0
Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition0
Mixtures of Deep Neural Experts for Automated Speech Scoring0
MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification0
MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition0
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition0
MLP-based architecture with variable length input for automatic speech recognition0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets0
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark0
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition0
MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones0
Mobile big data analysis with machine learning0
Mobile Keyboard Input Decoding with Finite-State Transducers0
Mobility Enhancement for Elderly0
MobiVSR: A Visual Speech Recognition Solution for Mobile Devices0
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation0
Modality Attention for End-to-End Audio-visual Speech Recognition0
Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding0
Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features0
Modality Influence in Multimodal Machine Learning0
Model adaptation and adaptive training for the recognition of dysarthric speech0
Model Adaptation for ASR in low-resource Indian Languages0
Model-Based Approach for Measuring the Fairness in ASR0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified