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

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Showing 5951–6000 of 6433 papers

TitleStatusHype
AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR—0
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard—0
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers—0
A Voice Controlled E-Commerce Web Application—0
Avoid Overthinking in Self-Supervised Models for Speech Recognition—0
A Wavelet Transform Based Scheme to Extract Speech Pitch and Formant Frequencies—0
A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability—0
A Web Application for Automated Dialect Analysis—0
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings—0
A Winograd-based Integrated Photonics Accelerator for Convolutional Neural Networks—0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—0
Back from the future: bidirectional CTC decoding using future information in speech recognition—0
Back-Translation-Style Data Augmentation for End-to-End ASR—0
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—0
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech—0
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models—0
BANSpEmo: A Bangla Emotional Speech Recognition Dataset—0
BART based semantic correction for Mandarin automatic speech recognition system—0
BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR—0
Basque Speecon-like and Basque SpeechDat MDB-600: speech databases for the development of ASR technology for Basque—0
BAT: Boundary aware transducer for memory-efficient and low-latency ASR—0
Batched Low-Rank Adaptation of Foundation Models—0
Batch-normalized joint training for DNN-based distant speech recognition—0
Batch Normalized Recurrent Neural Networks—0
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words—0
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition—0
Bayesian Neural Networks: An Introduction and Survey—0
Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling—0
Bayesian Reordering Model with Feature Selection—0
Bayesian Transformer Language Models for Speech Recognition—0
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction—0
BayesSpeech: A Bayesian Transformer Network for Automatic Speech Recognition—0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube—0
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder—0
Belief Hidden Markov Model for speech recognition—0
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics—0
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition—0
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech—0
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction—0
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR—0
Benchmarking Rotary Position Embeddings for Automatic Speech Recognition—0
Bengali Common Voice Speech Dataset for Automatic Speech Recognition—0
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model—0
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental—0
Best of Both Worlds: Multi-task Audio-Visual Automatic Speech Recognition and Active Speaker Detection—0
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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