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 33513400 of 6433 papers

TitleStatusHype
Accented Speech Recognition: A Survey0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era0
Fusing information streams in end-to-end audio-visual speech recognition0
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers0
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition0
Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors0
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
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
Conditional independence for pretext task selection in Self-supervised speech representation learningCode0
EAT: Enhanced ASR-TTS for Self-supervised Speech RecognitionCode0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation0
Experiments of ASR-based mispronunciation detection for children and adult English learners0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures0
A Toolbox for Construction and Analysis of Speech DatasetsCode1
NeMo Inverse Text Normalization: From Development To ProductionCode0
Innovative Bert-based Reranking Language Models for Speech Recognition0
Non-autoregressive Transformer-based End-to-end ASR using BERT0
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 20200
Accented Speech Recognition Inspired by Human Perception0
Language model fusion for streaming end to end speech recognition0
On Architectures and Training for Raw Waveform Feature Extraction in ASR0
Layer Reduction: Accelerating Conformer-Based Self-Supervised Model via Layer Consistency0
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset0
RNN Transducer Models For Spoken Language UnderstandingCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems0
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible InputsCode1
Capturing Multi-Resolution Context by Dilated Self-Attention0
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization0
Pushing the Limits of Non-Autoregressive Speech Recognition0
Librispeech Transducer Model with Internal Language Model Prior CorrectionCode1
Learning to Rank Microphones for Distant Speech RecognitionCode1
Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions0
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models0
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoringCode0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model0
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios0
Optimal Transport-based Adaptation in Dysarthric Speech Tasks0
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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