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 11511200 of 6433 papers

TitleStatusHype
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Distributed Learning of Deep Neural Networks using Independent Subnet TrainingCode0
Discrete Speech Unit Extraction via Independent Component AnalysisCode0
Enhancing Quantised End-to-End ASR Models via PersonalisationCode0
Discovering Phonetic Inventories with Crosslingual Automatic Speech RecognitionCode0
An Online Multilingual Hate speech Recognition SystemCode0
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found DataCode0
BERT Attends the Conversation: Improving Low-Resource Conversational ASRCode0
DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and LocalizationCode0
Direct Segmentation Models for Streaming Speech TranslationCode0
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst TasksCode0
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European LanguagesCode0
Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of WhisperCode0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
Calibrated Structured PredictionCode0
DEVI: Open-source Human-Robot Interface for Interactive Receptionist SystemsCode0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired ListenersCode0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition EncoderCode0
Exploring spectro-temporal features in end-to-end convolutional neural networksCode0
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
Differentiable Allophone Graphs for Language-Universal Speech RecognitionCode0
Deep word embeddings for visual speech recognitionCode0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech RecognitionCode0
Deep Learning Models in Speech Recognition: Measuring GPU Energy Consumption, Impact of Noise and Model Quantization for Edge DeploymentCode0
Boosting Cross-Domain Speech Recognition with Self-SupervisionCode0
Deep Learning using Linear Support Vector MachinesCode0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
Deep Learning for Audio Signal ProcessingCode0
DELTA: A DEep learning based Language Technology plAtformCode0
End-to-End Speech Recognition With Joint Dereverberation Of Sub-Band Autoregressive EnvelopesCode0
DeepEMO: Deep Learning for Speech Emotion RecognitionCode0
DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine ExtractionCode0
Deep-FSMN for Large Vocabulary Continuous Speech RecognitionCode0
Deep convolutional acoustic word embeddings using word-pair side informationCode0
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural NetworksCode0
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNsCode0
Decoding P300 Variability using Convolutional Neural NetworksCode0
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech ProcessingCode0
DecoderLens: Layerwise Interpretation of Encoder-Decoder TransformersCode0
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
Cascaded Cross-Modal Transformer for Audio-Textual ClassificationCode0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional DataCode0
Blank Collapse: Compressing CTC emission for the faster decodingCode0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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