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 1301–1350 of 6433 papers

TitleStatusHype
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
A Broadcast News Corpus for Evaluation and Tuning of German LVCSR Systems—0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
BayesSpeech: A Bayesian Transformer Network for Automatic Speech Recognition—0
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction—0
Bayesian Transformer Language Models for Speech Recognition—0
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
Bayesian Reordering Model with Feature Selection—0
An End-to-end Architecture of Online Multi-channel Speech Separation—0
Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling—0
Bayesian Neural Networks: An Introduction and Survey—0
An Empirical Study of Language Model Integration for Transducer based Speech Recognition—0
A Deep Dive into Deep Cluster—0
A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition—0
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition—0
An Empirical Study of Efficient ASR Rescoring with Transformers—0
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words—0
An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition—0
A Decidability-Based Loss Function—0
Batch Normalized Recurrent Neural Networks—0
Batch-normalized joint training for DNN-based distant speech recognition—0
An empirical assessment of deep learning approaches to task-oriented dialog management—0
Batched Low-Rank Adaptation of Foundation Models—0
BAT: Boundary aware transducer for memory-efficient and low-latency ASR—0
An Empirical Analysis of Deep Audio-Visual Models for Speech Recognition—0
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons—0
A brief history of AI: how to prevent another winter (a critical review)—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
Basque Speecon-like and Basque SpeechDat MDB-600: speech databases for the development of ASR technology for Basque—0
BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR—0
BART based semantic correction for Mandarin automatic speech recognition system—0
An efficient text augmentation approach for contextualized Mandarin speech recognition—0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings—0
BANSpEmo: A Bangla Emotional Speech Recognition Dataset—0
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models—0
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems—0
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—0
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks—0
Adding Connectionist Temporal Summarization into Conformer to Improve Its Decoder Efficiency For Speech Recognition—0
An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation—0
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM—0
Back-Translation-Style Data Augmentation for End-to-End ASR—0
AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation—0
An Effective Training Framework for Light-Weight Automatic Speech Recognition Models—0
Back from the future: bidirectional CTC decoding using future information in speech recognition—0
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts—0
A comparison of streaming models and data augmentation methods for robust speech recognition—0
A Bilingual Interactive Human Avatar Dialogue System—0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—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