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 56015650 of 6433 papers

TitleStatusHype
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models0
End-to-end attention-based distant speech recognition with Highway LSTM0
Achieving Human Parity in Conversational Speech Recognition0
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition0
Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition0
Very Deep Convolutional Networks for End-to-End Speech RecognitionCode0
Latent Sequence Decompositions0
A Gentle Tutorial of Recurrent Neural Network with Error BackpropagationCode0
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech0
Challenges of Computational Processing of Code-Switching0
QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingCode0
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection0
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models0
Semi-supervised Learning with Sparse Autoencoders in Phone Classification0
Very Deep Convolutional Neural Networks for Robust Speech RecognitionCode0
Retrieval Term Prediction Using Deep Learning Methods0
Measuring Diversified Proficiency of Japanese Learners of English0
A Generalized Framework for Hierarchical Word Sequence Language Model0
A Tour of TensorFlow0
使用字典學習法於強健性語音辨識(The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]0
融合多任務學習類神經網路聲學模型訓練於會議語音辨識之研究(Leveraging Multi-task Learning with Neural Network Based Acoustic Modeling for Improved Meeting Speech Recognition) [In Chinese]0
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks0
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition0
OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline0
Multi-task Recurrent Model for True Multilingual Speech Recognition0
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task LearningCode0
Minimally Supervised Written-to-Spoken Text Normalization0
Advances in All-Neural Speech Recognition0
Interactive Spoken Content Retrieval by Deep Reinforcement Learning0
An Adaptive Psychoacoustic Model for Automatic Speech Recognition0
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks0
The Microsoft 2016 Conversational Speech Recognition System0
Purely sequence-trained neural networks for ASR based on lattice-free MMI0
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms0
Task Lineages: Dialog State Tracking for Flexible Interaction0
On the verbalization patterns of part-whole relations in isiZulu0
Generating sets of related sentences from input seed features0
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications0
Socially-Aware Animated Intelligent Personal Assistant Agent0
Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms0
Reward Augmented Maximum Likelihood for Neural Structured Prediction0
Temperature-Based Deep Boltzmann Machines0
Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models for Reverberant Speech Recognition0
Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding0
Learning Online Alignments with Continuous Rewards Policy Gradient0
Efficient Segmental Cascades for Speech Recognition0
Knowledge Distillation for Small-footprint Highway Networks0
N-gram language models for massively parallel devices0
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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