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 501–550 of 6433 papers

TitleStatusHype
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
The History of Speech Recognition to the Year 2030Code1
CB-Conformer: Contextual biasing Conformer for biased word recognitionCode1
Token-Level Supervised Contrastive Learning for Punctuation RestorationCode1
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing ModelsCode1
Towards Building an End-to-End Multilingual Automatic Lyrics Transcription ModelCode1
Towards Resistant Audio Adversarial ExamplesCode1
Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of SoundCode1
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
Towards Voice Reconstruction from EEG during Imagined SpeechCode1
CoVoST 2 and Massively Multilingual Speech-to-Text TranslationCode1
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimationCode1
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural NetworksCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation WritingCode1
BIG-C: a Multimodal Multi-Purpose Dataset for BembaCode1
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
BembaSpeech: A Speech Recognition Corpus for the Bemba LanguageCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNetCode1
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and RecognitionCode1
Automatic Speech Recognition Benchmark for Air-Traffic CommunicationsCode1
Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task LearningCode1
Automatic Speech Recognition for Speech Assessment of Persian Preschool ChildrenCode1
AVATAR: Unconstrained Audiovisual Speech RecognitionCode1
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
Can Contextual Biasing Remain Effective with Whisper and GPT-2?Code1
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling InsightsCode1
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
AV Taris: Online Audio-Visual Speech RecognitionCode1
Back Translation for Speech-to-text Translation Without TranscriptsCode1
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithmCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
AutoDiCE: Fully Automated Distributed CNN Inference at the EdgeCode1
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0Code1
Bridging the Gaps of Both Modality and Language: Synchronous Bilingual CTC for Speech Translation and Speech RecognitionCode1
Bridging the Granularity Gap for Acoustic ModelingCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
CAPE: Encoding Relative Positions with Continuous Augmented Positional EmbeddingsCode1
CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command RecognitionCode1
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command RecognitionCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact CentersCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
Attention-Based Models for Speech RecognitionCode1
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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 MMIPercentage error12.9—Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN BMMIPercentage error12.9—Unverified
9DNN MPEPercentage error12.9—Unverified
10Deep Speech + FSHPercentage 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
4test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
5Deep Speech 2Word 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