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 40014050 of 6433 papers

TitleStatusHype
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable ProjectionCode0
Video Super Resolution Based on Deep Learning: A Comprehensive Survey0
Consistent Transcription and Translation of SpeechCode0
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR0
Sequential Routing Framework: Fully Capsule Network-based Speech RecognitionCode0
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models0
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition0
DiffRNN: Differential Verification of Recurrent Neural Networks0
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations0
SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks0
Comparative Analysis of Polynomial and Rational Approximations of Hyperbolic Tangent Function for VLSI Implementation0
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems0
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation0
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results0
Class LM and word mapping for contextual biasing in End-to-End ASR0
Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters0
Robust Prediction of Punctuation and Truecasing for Medical ASR0
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition0
Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning0
CacheNet: A Model Caching Framework for Deep Learning Inference on the Edge0
The AFRL IWSLT 2020 Systems: Work-From-Home Edition0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo, and Wolaytta0
How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems0
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions0
Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation0
Applications of Natural Language Processing in Bilingual Language Teaching: An Indonesian-English Case Study0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning0
CUNI Neural ASR with Phoneme-Level Intermediate Step for -Native at IWSLT 20200
Multimodal and Multiresolution Speech Recognition with Transformers0
Whole-Word Segmental Speech Recognition with Acoustic Word EmbeddingsCode0
Towards Understanding ASR Error Correction for Medical Conversations0
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations0
Tigrinya Automatic Speech recognition with Morpheme based recognition units0
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University0
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity0
Robust Neural Machine Translation with ASR Errors0
Multi-Task Variational Information Bottleneck0
SimulSpeech: End-to-End Simultaneous Speech to Text Translation0
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition0
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic MemoryCode0
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion0
Streaming Transformer ASR with Blockwise Synchronous Inference0
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals0
One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble0
Self-Supervised Representations Improve End-to-End Speech Translation0
Bayesian Neural Networks: An Introduction and Survey0
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition0
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples0
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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