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 52015250 of 6433 papers

TitleStatusHype
Deep Learning for Forecasting Stock Returns in the Cross-Section0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer0
Deep Learning: A Critical AppraisalCode0
Training RNNs as Fast as CNNsCode2
Gated ConvNets for Letter-Based ASR0
Large Scale Multi-Domain Multi-Task Learning with MultiModel0
Fast Node Embeddings: Learning Ego-Centric Representations0
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation0
Clipping Free Attacks Against Neural Networks0
Link Weight Prediction with Node Embeddings0
Monotonic Chunkwise AttentionCode1
New Baseline in Automatic Speech Recognition for Northern S\'ami0
Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages0
Detecting Institutional Dialog Acts in Police Traffic Stops0
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction0
PronouncUR: An Urdu Pronunciation Lexicon Generator0
The CAPIO 2017 Conversational Speech Recognition System0
What do we need to build explainable AI systems for the medical domain?0
Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks0
Letter-Based Speech Recognition with Gated ConvNetsCode0
On Using Backpropagation for Speech Texture Generation and Voice Conversion0
Use of Deep Learning in Modern Recommendation System: A Summary of Recent WorksCode0
Improved Regularization Techniques for End-to-End Speech Recognition0
Improving End-to-End Speech Recognition with Policy Learning0
Subword and Crossword Units for CTC Acoustic Models0
Deep Learning for Distant Speech Recognition0
A Berkeley View of Systems Challenges for AI0
Monotonic Chunkwise AttentionCode1
FFT-Based Deep Learning Deployment in Embedded Systems0
Learning Robust Dialog Policies in Noisy Environments0
Building competitive direct acoustics-to-word models for English conversational speech recognition0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
An analysis of incorporating an external language model into a sequence-to-sequence model0
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model0
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence ModelsCode1
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
State-of-the-art Speech Recognition With Sequence-to-Sequence ModelsCode1
NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs0
Phonemic Transcription of Low-Resource Tonal LanguagesCode0
Improving End-to-End Memory Networks with Unified Weight Tying0
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples0
Deep Learning Scaling is Predictable, Empirically0
Visual Features for Context-Aware Speech Recognition0
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN FeaturesCode0
Exploiting Nontrivial Connectivity for Automatic Speech Recognition0
Language Bootstrapping: Learning Word Meanings From Perception-Action AssociationCode0
A Deep Relevance Matching Model for Ad-hoc RetrievalCode0
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust 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