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 4751–4800 of 6433 papers

TitleStatusHype
Training for Speech Recognition on Coprocessors—0
Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification—0
Training Neural Networks using SAT solvers—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Training Recurrent Neural Networks against Noisy Computations during Inference—0
Training Speech Enhancement Systems with Noisy Speech Datasets—0
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework—0
Training variance and performance evaluation of neural networks in speech—0
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers—0
Trait-Based Hypothesis Selection For Machine Translation—0
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications—0
TranscRater: a Tool for Automatic Speech Recognition Quality Estimation—0
Transcribe, Align and Segment: Creating speech datasets for low-resource languages—0
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR—0
Transcribing and Translating, Fast and Slow: Joint Speech Translation and Recognition—0
Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos—0
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition—0
Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation—0
Trans-dimensional Random Fields for Language Modeling—0
Transducer-Llama: Integrating LLMs into Streamable Transducer-based Speech Recognition—0
Transducers with Pronunciation-aware Embeddings for Automatic Speech Recognition—0
Transferable Adversarial Attacks against ASR—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification—0
Transfer Learning Approaches for Streaming End-to-End Speech Recognition System—0
Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments—0
Transfer Learning for Algorithm Recommendation—0
Transfer Learning for British Sign Language Modelling—0
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic—0
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping—0
Transfer Learning for Speech and Language Processing—0
Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations—0
Transfer Learning from Audio-Visual Grounding to Speech Recognition—0
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization—0
Transfer Learning from Whisper for Microscopic Intelligibility Prediction—0
Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak—0
Transferring Knowledge from a RNN to a DNN—0
Transformer ASR with Contextual Block Processing—0
Transformer-based Acoustic Modeling for Hybrid Speech Recognition—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project—0
Transformer Based Deliberation for Two-Pass Speech Recognition—0
Transformer-based end-to-end speech recognition with residual Gaussian-based self-attention—0
Transformer-based language modeling and decoding for conversational speech recognition—0
Transformer-based Model for ASR N-Best Rescoring and Rewriting—0
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture—0
Transformer-based Streaming ASR with Cumulative Attention—0
Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video—0
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications—0
Show:102550
← PrevPage 96 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