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 401450 of 6433 papers

TitleStatusHype
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-TrainingCode1
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and SyllablesCode1
Interactive Feature Fusion for End-to-End Noise-Robust Speech RecognitionCode1
Long Expressive Memory for Sequence ModelingCode1
Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED DatasetCode1
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech RecognitionCode1
FAST-RIR: Fast neural diffuse room impulse response generatorCode1
Late reverberation suppression using U-netsCode1
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken ConversationsCode1
Factorized Neural Transducer for Efficient Language Model AdaptationCode1
SD-QA: Spoken Dialectal Question Answering for the Real WorldCode1
AI Accelerator Survey and TrendsCode1
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech RecognitionCode1
Vietnamese end-to-end speech recognition using wav2vec 2.0Code1
You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event DetectionCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent ClassificationCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
The History of Speech Recognition to the Year 2030Code1
USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition ExperimentsCode1
SVEva Fair: A Framework for Evaluating Fairness in Speaker VerificationCode1
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0Code1
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural NetworksCode1
Token-Level Supervised Contrastive Learning for Punctuation RestorationCode1
STRODE: Stochastic Boundary Ordinary Differential EquationCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
CLSRIL-23: Cross Lingual Speech Representations for Indic LanguagesCode1
Layer-wise Analysis of a Self-supervised Speech Representation ModelCode1
Kosp2e: Korean Speech to English Translation CorpusCode1
TENET: A Time-reversal Enhancement Network for Noise-robust ASRCode1
Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech RecognitionCode1
Combining Frame-Synchronous and Label-Synchronous Systems for Speech RecognitionCode1
MeshRIR: A Dataset of Room Impulse Responses on Meshed Grid Points For Evaluating Sound Field Analysis and Synthesis MethodsCode1
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
RyanSpeech: A Corpus for Conversational Text-to-Speech SynthesisCode1
Learning Audio-Visual DereverberationCode1
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden UnitsCode1
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed AudioCode1
Incorporating External POS Tagger for Punctuation RestorationCode1
CAPE: Encoding Relative Positions with Continuous Augmented Positional EmbeddingsCode1
Lightweight Adapter Tuning for Multilingual Speech TranslationCode1
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling InsightsCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech TranslationCode1
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of ExpertsCode1
Software Engineering for AI-Based Systems: A SurveyCode1
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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