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 48014850 of 6433 papers

TitleStatusHype
Modality Attention for End-to-End Audio-visual Speech Recognition0
Exploring RNN-Transducer for Chinese Speech Recognition0
An Online Attention-based Model for Speech Recognition0
Multi-encoder multi-resolution framework for end-to-end speech recognition0
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition0
Vectorization of hypotheses and speech for faster beam search in encoder decoder-based speech recognition0
Analyzing deep CNN-based utterance embeddings for acoustic model adaptation0
Stream attention-based multi-array end-to-end speech recognition0
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition0
Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling0
Multimodal Grounding for Sequence-to-Sequence Speech RecognitionCode0
Few-shot learning with attention-based sequence-to-sequence models0
Confusion2Vec: Towards Enriching Vector Space Word Representations with Representational Ambiguities0
RNNFast: An Accelerator for Recurrent Neural Networks Using Domain Wall Memory0
Towards Fluent Translations from Disfluent Speech0
Analysis of Multilingual Sequence-to-Sequence speech recognition systems0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments0
Hierarchical Neural Network Architecture In Keyword Spotting0
Language model integration based on memory control for sequence to sequence speech recognition0
Reconstructing Speech Stimuli From Human Auditory Cortex Activity Using a WaveNet Approach0
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech RecognitionCode0
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech RecognitionCode0
Discriminative training of RNNLMs with the average word error criterion0
Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation0
The Marchex 2018 English Conversational Telephone Speech Recognition System0
When CTC Training Meets Acoustic Landmarks0
End-to-End Monaural Multi-speaker ASR System without Pretraining0
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization0
Pushing the boundaries of audiovisual word recognition using Residual Networks and LSTMs0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation0
Improving the Robustness of Speech Translation0
Cycle-consistency training for end-to-end speech recognition0
`Indicatements' that character language models learn English morpho-syntactic units and regularities0
On the End-to-End Solution to Mandarin-English Code-switching Speech RecognitionCode0
Introspection for convolutional automatic speech recognition0
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition0
Visualizing Group Dynamics based on Multiparty Meeting Understanding0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example0
PizzaPal: Conversational Pizza Ordering using a High-Density Conversational AI Platform0
Tropical Modeling of Weighted Transducer Algorithms on Graphs0
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator0
Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition0
Towards End-to-End Code-Switching Speech Recognition0
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English0
Generative Adversarial Networks for Unpaired Voice Transformation on Impaired SpeechCode0
Bi-Directional Lattice Recurrent Neural Networks for Confidence EstimationCode0
Towards End-to-end Automatic Code-Switching Speech Recognition0
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition0
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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