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 39013950 of 6433 papers

TitleStatusHype
Zero-shot Speech Translation0
Zero Shot Text to Speech Augmentation for Automatic Speech Recognition on Low-Resource Accented Speech Corpora0
Zipformer: A faster and better encoder for automatic speech recognition0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities0
0/1 Deep Neural Networks via Block Coordinate Descent0
Joint CTC/attention decoding for end-to-end speech recognition0
Joint Encoder-Decoder Self-Supervised Pre-training for ASR0
Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech0
Joint Incremental Disfluency Detection and Dependency Parsing0
Joint Language and Translation Modeling with Recurrent Neural Networks0
Joint Learning from Labeled and Unlabeled Data for Information Retrieval0
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks0
Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks0
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator0
Joint Learning of Phonetic Units and Word Pronunciations for ASR0
Jointly Trained Transformers models for Spoken Language Translation0
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition0
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization0
Joint On-line Learning of a Zero-shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager0
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation0
Joint Part-of-Speech and Language ID Tagging for Code-Switched Data0
Joint Satisfaction of Syntactic and Pragmatic Constraints Improves Incremental Spoken Language Understanding0
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers0
Joint Speech Recognition and Audio Captioning0
Joint Speech Recognition and Speaker Diarization via Sequence Transduction0
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR0
Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts0
Joint unsupervised and supervised learning for context-aware language identification0
Joint Unsupervised and Supervised Training for Multilingual ASR0
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization0
Joint vs Sequential Speaker-Role Detection and Automatic Speech Recognition for Air-traffic Control0
Joint Word Segmentation and Phonetic Category Induction0
k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning0
Kaggle Competition: Cantonese Audio-Visual Speech Recognition for In-car Commands0
Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition0
Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN0
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition0
Keep Decoding Parallel with Effective Knowledge Distillation from Language Models to End-to-end Speech Recognisers0
Kernel Approximation Methods for Speech Recognition0
Key Event Detection in Video using ASR and Visual Data0
Keynote: Graph-based Approaches for Spoken Language Understanding0
Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures0
Keyphrase Prediction from Video Transcripts: New Dataset and Directions0
Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking0
Keyword-Guided Adaptation of Automatic Speech Recognition0
Keyword spotting -- Detecting commands in speech using deep learning0
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults0
KinSPEAK: Improving speech recognition for Kinyarwanda via semi-supervised learning methods0
Kite: Automatic speech recognition for unmanned aerial vehicles0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning0
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