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 38013850 of 6433 papers

TitleStatusHype
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter0
Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things0
Deep Shallow Fusion for RNN-T Personalization0
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information0
11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot0
Towards Semi-Supervised Semantics Understanding from Speech0
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults0
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments0
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS0
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR0
Personalized Query Rewriting in Conversational AI Agents0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework0
Efficient End-to-End Speech Recognition Using Performers in Conformers0
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition0
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers0
Stochastic Attention Head Removal: A simple and effective method for improving Transformer Based ASR ModelsCode0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration0
Resource-Constrained Federated Learning with Heterogeneous Labels and Models0
Domain Adaptation Using Class Similarity for Robust Speech RecognitionCode0
Alignment Restricted Streaming Recurrent Neural Network Transducer0
Multi-Accent Adaptation based on Gate Mechanism0
Data Augmentation for End-to-end Code-switching Speech Recognition0
Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework0
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition0
Paralinguistic Privacy Protection at the Edge0
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time0
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition0
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features0
Dynamic latency speech recognition with asynchronous revision0
Improving RNN transducer with normalized jointer network0
Warped Language Models for Noise Robust Language Understanding0
Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion0
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition0
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis0
DNN-Based Semantic Model for Rescoring N-best Speech Recognition List0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation0
Focus on the present: a regularization method for the ASR source-target attention layer0
Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition0
Direct Segmentation Models for Streaming Speech TranslationCode0
May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance0
ELITR: European Live Translator0
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others0
Simultaneous Translation0
Effectively pretraining a speech translation decoder with Machine Translation data0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training0
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced LanguagesCode0
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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