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 4051–4100 of 6433 papers

TitleStatusHype
Semi-supervised ASR by End-to-end Self-training—0
Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
Semi-supervised Learning with Sparse Autoencoders in Phone Classification—0
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition—0
Semi-Supervised Speech Recognition via Graph-based Temporal Classification—0
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages—0
Semi-tied Units for Efficient Gating in LSTM and Highway Networks—0
SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors—0
Senone-aware Adversarial Multi-task Training for Unsupervised Child to Adult Speech Adaptation—0
Sentence Boundary Augmentation For Neural Machine Translation Robustness—0
Sentence Compression For Automatic Subtitling—0
Sentence segmentation of aphasic speech—0
Sentence selection for automatic scoring of Mandarin proficiency—0
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition—0
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities—0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter—0
SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation—0
Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech—0
Sequence-based Multi-lingual Low Resource Speech Recognition—0
Sequence Discriminative Training for Deep Learning based Acoustic Keyword Spotting—0
Sequence-level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models—0
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition—0
Sequence-level self-learning with multiple hypotheses—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals—0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning—0
Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding—0
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition—0
Sequence-to-sequence models in peer-to-peer learning: A practical application—0
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions—0
Sequence Training and Adaptation of Highway Deep Neural Networks—0
Sequence Transduction with Graph-based Supervision—0
Sequential Editing for Lifelong Training of Speech Recognition Models—0
Sequential End-to-End Intent and Slot Label Classification and Localization—0
Sequential Labeling for Tracking Dynamic Dialog States—0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution—0
Serialized Output Training by Learned Dominance—0
Serialized Output Training for End-to-End Overlapped Speech Recognition—0
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition—0
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants—0
Session-level Language Modeling for Conversational Speech—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
調變頻譜分解之改良於強健性語音辨識(Several Refinements of Modulation Spectrum Factorization for Robust Speech Recognition) [In Chinese]—0
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
室內遠距離語音辨識實驗(Experiments on In-House Far-Field Speech Recognition)—0
使用長短期記憶類神經網路建構中文語音辨識器之研究 (A Study on Mandarin Speech Recognition using Long Short- Term Memory Neural Network)—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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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