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 32013250 of 6433 papers

TitleStatusHype
Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer0
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages0
Multilingual End-to-End Speech Translation0
Multilingual Parallel Corpus for Global Communication Plan0
Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching0
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling0
Multilingual Speech Recognition for Low-Resource Indian Languages using Multi-Task conformer0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes0
Multilingual Speech Recognition With A Single End-To-End Model0
Multilingual Speech Recognition with Corpus Relatedness Sampling0
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 20210
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 20210
Multilingual Standalone Trustworthy Voice-Based Social Network for Disaster Situations0
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model0
Multilingual training set selection for ASR in under-resourced Malian languages0
Multilingual Transfer Learning for Children Automatic Speech Recognition0
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages0
Multilingual Word Error Rate Estimation: e-WER30
Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models0
Multimodal and Multiresolution Speech Recognition with Transformers0
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding0
Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction0
Multimodal Corpora for Silent Speech Interaction0
Multimodal Corpus of Multi-party Conversations in Second Language0
Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models0
Multi-Modal Data Augmentation for End-to-End ASR0
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings0
Multi-Modal Detection of Alzheimer's Disease from Speech and Text0
Multi-modal embeddings using multi-task learning for emotion recognition0
Multimodal fusion via cortical network inspired losses0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications0
Multimodal Machine Learning: Integrating Language, Vision and Speech0
Multimodal Machine Translation through Visuals and Speech0
Multi-Modal Pre-Training for Automated Speech Recognition0
Multimodal Punctuation Prediction with Contextual Dropout0
Multimodal Representation Learning and Fusion0
Multi-Modal Retrieval For Large Language Model Based Speech Recognition0
Multimodal Short Video Rumor Detection System Based on Contrastive Learning0
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks0
Multimodal Speech Recognition with Unstructured Audio Masking0
Multi-modal Speech Transformer Decoders: When Do Multiple Modalities Improve Accuracy?0
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video0
Multi-mode Transformer Transducer with Stochastic Future Context0
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks0
Multi-pass Training and Cross-information Fusion for Low-resource End-to-end Accented Speech Recognition0
Multiple Confidence Gates For Joint Training Of SE And ASR0
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition0
Show:102550
← PrevPage 65 of 129Next →

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