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 33513400 of 6433 papers

TitleStatusHype
Technology-Augmented Multilingual Communication Models: New Interaction Paradigms, Shifts in the Language Services Industry, and Implications for Training Programs0
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental0
How Might We Create Better Benchmarks for Speech Recognition?0
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 20210
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 20210
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks0
ZJU’s IWSLT 2021 Speech Translation System0
A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers0
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning0
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations0
Differentiable Allophone Graphs for Language-Universal Speech RecognitionCode0
Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition0
OLR 2021 Challenge: Datasets, Rules and Baselines0
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition0
Semantic Communications for Speech Recognition0
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models0
Seed Words Based Data Selection for Language Model Adaptation0
On Prosody Modeling for ASR+TTS based Voice Conversion0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation0
A baseline model for computationally inexpensive speech recognition for Kazakh using the Coqui STT framework0
Multi-task Learning with Cross Attention for Keyword Spotting0
VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording0
A Configurable Multilingual Model is All You Need to Recognize All Languages0
Conformer-based End-to-end Speech Recognition With Rotary Position Embedding0
The IWSLT 2021 BUT Speech Translation Systems0
Zero-shot Speech Translation0
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems0
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings0
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition0
Noisy Training Improves E2E ASR for the Edge0
Representation Learning to Classify and Detect Adversarial Attacks against Speaker and Speech Recognition Systems0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers0
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning0
Improving Speech Recognition Accuracy of Local POI Using Geographical Models0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio0
Exploiting Single-Channel Speech For Multi-channel End-to-end Speech Recognition0
Improving a neural network model by explanation-guided training for glioma classification based on MRI data0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR0
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech RecognitionCode0
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation0
Arabic Code-Switching Speech Recognition using Monolingual Data0
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition0
Supervised Contrastive Learning for Accented 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