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 29012950 of 6433 papers

TitleStatusHype
Improved Mask-CTC for Non-Autoregressive End-to-End ASR0
Improved Meta Learning for Low Resource Speech Recognition0
Improved Meta-Learning Training for Speaker Verification0
改良調變頻譜統計圖等化法於強健性語音辨識之研究 (Improved Modulation Spectrum Histogram Equalization for Robust Speech Recognition) [In Chinese]0
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer0
Adversarial Machine Learning in Network Intrusion Detection Systems0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition0
Improved Regularization Techniques for End-to-End Speech Recognition0
Fast and Robust Neural Network Joint Models for Statistical Machine Translation0
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information0
Characterizing the Weight Space for Different Learning Models0
Improved Speech Enhancement with the Wave-U-Net0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit0
Improved Speech Representations with Multi-Target Autoregressive Predictive Coding0
Fast and parallel decoding for transducer0
Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation0
Fast and Accurate Reordering with ITG Transition RNN0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement0
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations0
Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research0
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition0
Improve Sinhala Speech Recognition Through e2e LF-MMI Model0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis0
Improving Accented Speech Recognition with Multi-Domain Training0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion0
Fast and Accurate OOV Decoder on High-Level Features0
Improving Arabic Diacritization through Syntactic Analysis0
Improving ASR Contextual Biasing with Guided Attention0
Characterizing Audio Adversarial Examples Using Temporal Dependency0
Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging0
Improving Black-box Speech Recognition using Semantic Parsing0
Improving callsign recognition with air-surveillance data in air-traffic communication0
Character-aware audio-visual subtitling in context0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition0
Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts0
Improving child speech recognition with augmented child-like speech0
Improving Code-switched ASR with Linguistic Information0
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation0
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation0
Improving Continuous Sign Language Recognition: Speech Recognition Techniques and System Design0
Improving Continuous Sign Language Recognition with Cross-Lingual Signs0
Improving cross-domain n-gram language modelling with skipgrams0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation0
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization0
Improving CTC-based ASR Models with Gated Interlayer Collaboration0
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data0
Show:102550
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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