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 27512800 of 6433 papers

TitleStatusHype
Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question0
Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions0
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation0
Temporal Attention Augmented Transformer Hawkes Process0
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech0
Multi-Dialect Arabic Speech Recognition0
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition0
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
VoiceMoji: A Novel On-Device Pipeline for Seamless Emoji Insertion in Dictation0
Voice Quality and Pitch Features in Transformer-Based Speech Recognition0
Regularizing End-to-End Speech Translation with Triangular Decomposition AgreementCode1
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks0
Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching0
Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition0
Multi-turn RNN-T for streaming recognition of multi-party speech0
A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations0
Continual Learning for Monolingual End-to-End Automatic Speech RecognitionCode0
Automated Deep Learning: Neural Architecture Search Is Not the EndCode2
Self-Supervised Learning for speech recognition with Intermediate layer supervisionCode1
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems0
Real-Time Neural Voice Camouflage0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model0
On the Use of External Data for Spoken Named Entity RecognitionCode0
Robustifying automatic speech recognition by extracting slowly varying features0
ImportantAug: a data augmentation agent for speechCode0
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition0
Improving Speech Recognition on Noisy Speech via Speech Enhancement with Multi-Discriminators CycleGAN0
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks0
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech0
Sequence-level self-learning with multiple hypotheses0
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems0
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition0
Are E2E ASR models ready for an industrial usage?0
X-Vector based voice activity detection for multi-genre broadcast speech-to-textCode1
LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading0
A study on native American English speech recognition by Indian listeners with varying word familiarity level0
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMICode1
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge0
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking0
A Mixture of Expert Based Deep Neural Network for Improved ASR0
A higher order Minkowski loss for improved prediction ability of acoustic model in ASR0
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent0
An End-to-End Speech Recognition for the Nepali Language0
Analysis of Manipuri Tones in ManiTo: A Tonal Contrast Database0
Improve Sinhala Speech Recognition Through e2e LF-MMI Model0
IE-CPS Lexicon: An Automatic Speech Recognition Oriented Indian-English Pronunciation Dictionary0
Impact of Microphone position Measurement Error on Multi Channel Distant Speech Recognition & Intelligibility0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context0
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting0
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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
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord 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
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage 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
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord 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