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 35013550 of 6433 papers

TitleStatusHype
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders0
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech0
What shall we do with an hour of data? Speech recognition for the un- and under-served languages of Common Voice0
English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition0
Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech0
Efficient Weight factorization for Multilingual Speech Recognition0
Challenges and Obstacles Towards Deploying Deep Learning Models on Mobile Devices0
Accent Recognition with Hybrid Phonetic Features0
Performance Evaluation of Deep Convolutional Maxout Neural Network in Speech Recognition0
Streaming end-to-end speech recognition with jointly trained neural feature enhancement0
Hybrid Intelligence0
Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models0
On the limit of English conversational speech recognition0
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks0
Spectral modification for recognition of children’s speech undermismatched conditions0
Deformable TDNN with adaptive receptive fields for speech recognition0
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application0
Personalized Keyphrase Detection using Speaker and Environment Information0
On Addressing Practical Challenges for RNN-Transducer0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition0
Head-synchronous Decoding for Transformer-based Streaming ASR0
Quantization of Deep Neural Networks for Accurate Edge Computing0
Scalable End-to-End RF Classification: A Case Study on Undersized Dataset Regularization by Convolutional-MST0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models0
Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions0
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network0
Protecting gender and identity with disentangled speech representations0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning0
Accented Speech Recognition: A Survey0
On Sampling-Based Training Criteria for Neural Language Modeling0
Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers0
Discriminative Self-training for Punctuation Prediction0
Disfluency Detection with Unlabeled Data and Small BERT Models0
Scene-aware Far-field Automatic Speech Recognition0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era0
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers0
Fusing information streams in end-to-end audio-visual speech recognition0
Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors0
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems0
Multilingual and Cross-Lingual Intent Detection from Spoken Data0
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation0
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda NetworksCode0
Efficient and Generic 1D Dilated Convolution Layer for Deep LearningCode0
Conditional independence for pretext task selection in Self-supervised speech representation learningCode0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
Cross-domain Speech Recognition with Unsupervised Character-level Distribution MatchingCode0
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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