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 34513500 of 6433 papers

TitleStatusHype
Towards Lifelong Learning of End-to-end ASR0
Towards Lipreading Sentences with Active Appearance Models0
Towards Maximum Likelihood Training for Transducer-based Streaming Speech Recognition0
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions0
Towards measuring fairness in speech recognition: Fair-Speech dataset0
Continuous Silent Speech Recognition using EEG0
Towards Model-Size Agnostic, Compute-Free, Memorization-based Inference of Deep Learning0
Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System0
Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR0
Towards Online End-to-end Transformer Automatic Speech Recognition0
Towards Online Continuous Sign Language Recognition and Translation0
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation0
Towards Probing Contact Center Large Language Models0
Towards Processing of the Oral History Interviews and Related Printed Documents0
Towards Quantum Language Models0
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition0
Towards Robust Waveform-Based Acoustic Models0
Towards Selection of Text-to-speech Data to Augment ASR Training0
Towards Semi-Supervised Semantics Understanding from Speech0
Towards speech-to-text translation without speech recognition0
Towards spoken dialect identification of Irish0
Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation0
Towards Structured Deep Neural Network for Automatic Speech Recognition0
Towards Structured Deep Neural Network for Automatic Speech Recognition0
Towards the evaluation of automatic simultaneous speech translation from a communicative perspective0
Towards the Next Frontier in Speech Representation Learning Using Disentanglement0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods0
Towards the Universal Defense for Query-Based Audio Adversarial Attacks0
Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer0
Toward Streaming ASR with Non-Autoregressive Insertion-based Model0
Towards Understanding ASR Error Correction for Medical Conversations0
Towards Universal Speech Discrete Tokens: A Case Study for ASR and TTS0
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only0
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning0
Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network0
Towards Zero-Shot Code-Switched Speech Recognition0
Toward Zero Oracle Word Error Rate on the Switchboard Benchmark0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization0
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning0
Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach0
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment0
Traduction automatique \`a partir de corpus comparables: extraction de phrases parall\`eles \`a partir de donn\'ees comparables multimodales (Automatic Translation from Comparable corpora : extracting parallel sentences from multimodal comparable corpora) [in French]0
Trainable and Dynamic Computing: Error Backpropagation through Physical Media0
Training ASR models by Generation of Contextual Information0
Training Augmentation with Adversarial Examples for Robust Speech Recognition0
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision0
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments0
Training for Speech Recognition on Coprocessors0
Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification0
Training Neural Networks using SAT solvers0
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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