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

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Showing 4001–4050 of 6433 papers

TitleStatusHype
Language Modeling with Deep Transformers—0
Language Modeling with Functional Head Constraint for Code Switching Speech Recognition—0
Language Modeling with Highway LSTM—0
Language modeling with Neural trans-dimensional random fields—0
Language model integration based on memory control for sequence to sequence speech recognition—0
Language Models of Spoken Dutch—0
Language Recognition using Time Delay Deep Neural Network—0
Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically—0
Language-specific Characteristic Assistance for Code-switching Speech Recognition—0
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes—0
Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR—0
Language-universal phonetic encoder for low-resource speech recognition—0
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
Large Corpus of Czech Parliament Plenary Hearings—0
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition—0
Large Language Models based ASR Error Correction for Child Conversations—0
Large Language Models for Dysfluency Detection in Stuttered Speech—0
Large Language Models for Education: A Survey—0
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction—0
Large Linguistic Corpus Reduction with SCP Algorithms—0
Large Margin Neural Language Model—0
Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation—0
Large-scale discriminative language model reranking for voice-search—0
Large Scale Distributed Deep Networks—0
Large-Scale Domain Adaptation via Teacher-Student Learning—0
Large scale evaluation of importance maps in automatic speech recognition—0
Large-scale Language Model Rescoring on Long-form Data—0
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition—0
Large Scale Multi-Domain Multi-Task Learning with MultiModel—0
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model—0
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone—0
Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression—0
Large-Scale Syntactic Language Modeling with Treelets—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
Large-scale unsupervised audio pre-training for video-to-speech synthesis—0
Large-Scale Visual Speech Recognition—0
Large scale weakly and semi-supervised learning for low-resource video ASR—0
Large-vocabulary Audio-visual Speech Recognition in Noisy Environments—0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta—0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo, and Wolaytta—0
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning—0
Large Vocabulary Spontaneous Speech Recognition for Tigrigna—0
Late fusion ensembles for speech recognition on diverse input audio representations—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation—0
Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition—0
Latent Phrase Matching for Dysarthric Speech—0
Latent Sequence Decompositions—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified