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 10511100 of 6433 papers

TitleStatusHype
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst TasksCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech ToolkitCode0
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix AugmentationCode0
A Probabilistic Theory of Deep LearningCode0
Error-preserving Automatic Speech Recognition of Young English Learners' LanguageCode0
Evaluation of End-to-End Continuous Spanish Lipreading in Different Data ConditionsCode0
Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation NetworkCode0
End-to-End Speech Recognition With Joint Dereverberation Of Sub-Band Autoregressive EnvelopesCode0
Adversarial Example Detection by Classification for Deep Speech RecognitionCode0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy PreservationCode0
End-to-End Speech Recognition From the Raw WaveformCode0
End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model PretrainingCode0
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based ModelCode0
Enhancing Quantised End-to-End ASR Models via PersonalisationCode0
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification TasksCode0
End-to-End Attention-based Large Vocabulary Speech RecognitionCode0
End-to-end Audiovisual Speech RecognitionCode0
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern ArchitecturesCode0
End to End ASR System with Automatic Punctuation InsertionCode0
Emotional Speech Recognition with Pre-trained Deep Visual ModelsCode0
End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic HandsCode0
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda NetworksCode0
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language ModelsCode0
Application of Word2vec in Phoneme RecognitionCode0
Efficient and Generic 1D Dilated Convolution Layer for Deep LearningCode0
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural NetworkCode0
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic EmbeddingsCode0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correctionCode0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based DecodingCode0
Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech TasksCode0
Dysarthria Normalization via Local Lie Group Transformations for Robust ASRCode0
EAT: Enhanced ASR-TTS for Self-supervised Speech RecognitionCode0
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced LanguagesCode0
Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal ModelsCode0
DSD: Dense-Sparse-Dense Training for Deep Neural NetworksCode0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of WhisperCode0
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition NetworksCode0
Efficient Adaptation of Multilingual Models for Japanese ASRCode0
End-to-End Open Vocabulary Keyword Search With Multilingual Neural RepresentationsCode0
FastEmit: Low-latency Streaming ASR with Sequence-level Emission RegularizationCode0
Distributed Learning of Deep Neural Networks using Independent Subnet TrainingCode0
DoCIA: An Online Document-Level Context Incorporation Agent for Speech TranslationCode0
Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found DataCode0
An Overview of Multi-Task Learning in Deep Neural NetworksCode0
Do Deep Nets Really Need to be Deep?Code0
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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