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 41014150 of 6433 papers

TitleStatusHype
使用生成對抗網路於強健式自動語音辨識的應用(Exploiting Generative Adversarial Network for Robustness Automatic Speech Recognition)0
Shortcut Learning Susceptibility in Vision Classifiers0
Short-Term Projects, Long-Term Benefits: Four Student NLP Projects for Low-Resource Languages0
Short-Term Word-Learning in a Dynamically Changing Environment0
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition0
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions0
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning0
Signal Combination for Language Identification0
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation0
Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition0
Silent Speech Interfaces for Speech Restoration: A Review0
Silent versus modal multi-speaker speech recognition from ultrasound and video0
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research0
Similarity and Content-based Phonetic Self Attention for Speech Recognition0
Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference0
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test0
Simple and Effective Unsupervised Speech Synthesis0
Simple and Effective Unsupervised Speech Translation0
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning0
Simplified End-to-End MMI Training and Voting for ASR0
Simplified guidelines for the creation of Large Scale Dialectal Arabic Annotations0
Simplified Self-Attention for Transformer-based End-to-End Speech Recognition0
Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition0
Simulating ASR errors for training SLU systems0
Simulating Early-Termination Search for Verbose Spoken Queries0
Simulating realistic speech overlaps improves multi-talker ASR0
SimulSpeech: End-to-End Simultaneous Speech to Text Translation0
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models0
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS0
Simultaneous Translation0
Singing voice conversion with non-parallel data0
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training0
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard0
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition0
Situated Incremental Natural Language Understanding using a Multimodal, Linguistically-driven Update Model0
SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping0
Skipformer: A Skip-and-Recover Strategy for Efficient Speech Recognition0
SLAM-AAC: Enhancing Audio Captioning with Paraphrasing Augmentation and CLAP-Refine through LLMs0
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition0
Sliding Alignment Windows for Real-Time Crowd Captioning0
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling0
SLM: Bridge the thin gap between speech and text foundation models0
SLPAT in practice: lessons from translational research0
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks0
Small energy masking for improved neural network training for end-to-end speech recognition0
Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition0
Small-footprint Highway Deep Neural Networks for Speech Recognition0
Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier0
Show:102550
← PrevPage 83 of 129Next →

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