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 29513000 of 6433 papers

TitleStatusHype
Fast and Accurate OOV Decoder on High-Level Features0
Improving Deep Learning through Automatic Programming0
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation0
Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training0
Improving EEG based Continuous Speech Recognition0
Characterizing Audio Adversarial Examples Using Temporal Dependency0
Improving Efficiency in Large-Scale Decentralized Distributed Training0
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 End-to-End Memory Networks with Unified Weight Tying0
Improving End-to-End Models for Set Prediction in Spoken Language Understanding0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis0
Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling0
Improving End-to-End Speech Recognition with Policy Learning0
Improving End-to-End Speech-to-Intent Classification with Reptile0
Character-aware audio-visual subtitling in context0
Improving Fast-slow Encoder based Transducer with Streaming Deliberation0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition0
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation0
Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model0
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech0
Improving Joint Speech-Text Representations Without Alignment0
Improving Language Identification of Accented Speech0
Improving Language Model Adaptation using Automatic Data Selection and Neural Network0
Improving Language Model Integration for Neural Machine Translation0
Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer0
Improving low-resource ASR performance with untranscribed out-of-domain data0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS0
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training0
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data0
Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community0
Character-Aware Attention-Based End-to-End Speech Recognition0
FARMI: A FrAmework for Recording Multi-Modal Interactions0
Improving Massively Multilingual ASR With Auxiliary CTC Objectives0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model0
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction0
Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations0
Improving Multi-task Learning via Seeking Task-based Flat Regions0
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop0
Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling0
Improving Named Entity Transcription with Contextual LLM-based Revision0
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation0
Improving Neural Language Models with Weight Norm Initialization and Regularization0
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study0
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution0
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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