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 45514600 of 6433 papers

TitleStatusHype
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech0
Multi-task Language Modeling for Improving Speech Recognition of Rare Words0
Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition0
Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction0
Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition0
Multitask Learning for Low Resource Spoken Language Understanding0
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention0
Multi-task Learning with Cross Attention for Keyword Spotting0
Multitask Learning with CTC and Segmental CRF for Speech Recognition0
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition0
Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs0
Multi-task Recurrent Model for True Multilingual Speech Recognition0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding0
Multi-Task Self-Supervised Pre-Training for Music Classification0
Multitask Training with Text Data for End-to-End Speech Recognition0
Multi-Task Variational Information Bottleneck0
Multi-task Voice Activated Framework using Self-supervised Learning0
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders0
Multi-Temporal Lip-Audio Memory for Visual Speech Recognition0
Multi-turn RNN-T for streaming recognition of multi-party speech0
Multi-user VoiceFilter-Lite via Attentive Speaker Embedding0
Multi-view Attention-based Speech Enhancement Model for Noise-robust Automatic Speech Recognition0
Multi-View Frequency-Attention Alternative to CNN Frontends for Automatic Speech Recognition0
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition0
Multiword Expressions and the Low-Resource Scenario from the Perspective of a Local Oral Culture0
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling0
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition0
MuST-C: a Multilingual Speech Translation Corpus0
Mutually-Constrained Monotonic Multihead Attention for Online ASR0
MVA: The Multimodal Virtual Assistant0
My Science Tutor---Learning Science with a Conversational Virtual Tutor0
My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech0
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy0
NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space0
NCSU\_SAS\_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization0
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems0
NeMo: a toolkit for building AI applications using Neural Modules0
Nepali Speech Recognition Using CNN, GRU and CTC0
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training0
NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs0
Neural approaches to spoken content embedding0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework0
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection0
Neural Blind Source Separation and Diarization for Distant Speech Recognition0
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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