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
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR0
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings0
Back from the future: bidirectional CTC decoding using future information in speech recognition0
Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition0
Enabling On-Device Training of Speech Recognition Models with Federated Dropout0
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.00
Analyzing the Robustness of Unsupervised Speech Recognition0
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution0
Spell my name: keyword boosted speech recognition0
CTC Variations Through New WFST Topologies0
Parallel Composition of Weighted Finite-State Transducers0
Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition0
Integrating Categorical Features in End-to-End ASR0
Is Attention always needed? A Case Study on Language Identification from Speech0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition0
BERT Attends the Conversation: Improving Low-Resource Conversational ASRCode0
ASR Rescoring and Confidence Estimation with ELECTRA0
Building a Noisy Audio Dataset to Evaluate Machine Learning Approaches for Automatic Speech Recognition Systems0
Towards efficient end-to-end speech recognition with biologically-inspired neural networks0
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech0
Multi-task Voice Activated Framework using Self-supervised Learning0
Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning0
Chinese Medical Speech Recognition with Punctuated Hypothesis0
Employing low-pass filtered temporal speech features for the training of ideal ratio mask in speech enhancement0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems0
Data centric approach to Chinese Medical Speech Recognition0
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device0
Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection0
Improving Punctuation Restoration for Speech Transcripts via External Data0
SpliceOut: A Simple and Efficient Audio Augmentation Method0
Federated Learning in ASR: Not as Easy as You ThinkCode0
PhaseFool: Phase-oriented Audio Adversarial Examples via Energy Dissipation0
Speech-MLP: a simple MLP architecture for speech processing0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch0
Will a Blind Model Hear Better? Advanced Audiovisual Recognition System with Brain-Like Compensating and Gating0
Conditioning Sequence-to-sequence Networks with Learned Activations0
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable0
Synthesising Audio Adversarial Examples for Automatic Speech Recognition0
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards0
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems0
Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks0
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness0
MLP-based architecture with variable length input for automatic speech recognition0
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN0
Understanding the Role of Self Attention for Efficient Speech Recognition0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition0
Word-level confidence estimation for RNN transducers0
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