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 41014150 of 6433 papers

TitleStatusHype
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition0
Relative Positional Encoding for Speech Recognition and Direct Translation0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
Investigation of Large-Margin Softmax in Neural Language Modeling0
Early Stage LM Integration Using Local and Global Log-Linear Combination0
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion0
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI0
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces0
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech RecognitionCode0
Exploring Transformers for Large-Scale Speech Recognition0
Iterative Pseudo-Labeling for Speech RecognitionCode0
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models0
Quaternion Neural Networks for Multi-channel Distant Speech Recognition0
Attention-based Transducer for Online Speech Recognition0
An Effective End-to-End Modeling Approach for Mispronunciation Detection0
Audio-visual Multi-channel Recognition of Overlapped Speech0
Weak-Attention Suppression For Transformer Based Speech Recognition0
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation0
Large scale weakly and semi-supervised learning for low-resource video ASR0
Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss0
Dynamic Sparsity Neural Networks for Automatic Speech Recognition0
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages0
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition0
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation0
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition0
Context-Dependent Acoustic Modeling without Explicit Phone Clustering0
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model0
Coupled Training of Sequence-to-Sequence Models for Accented Speech RecognitionCode0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation0
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation0
DiscreTalk: Text-to-Speech as a Machine Translation Problem0
Automatic Estimation of Intelligibility Measure for Consonants in Speech0
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition0
Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)Code0
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices0
Incremental Learning for End-to-End Automatic Speech Recognition0
The Perceptimatic English Benchmark for Speech Perception Models0
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions0
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training0
Community Detection Clustering via Gumbel Softmax0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition0
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality0
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech0
A language score based output selection method for multilingual speech recognition0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta0
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications0
Where are we in Named Entity Recognition from Speech?0
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu0
Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains0
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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