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
Challenges of Computational Processing of Code-Switching0
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural NetworksCode1
QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingCode0
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks0
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection0
Semi-supervised Learning with Sparse Autoencoders in Phone Classification0
Very Deep Convolutional Neural Networks for Robust Speech RecognitionCode0
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
A Generalized Framework for Hierarchical Word Sequence Language Model0
Retrieval Term Prediction Using Deep Learning Methods0
Measuring Diversified Proficiency of Japanese Learners of English0
A Tour of TensorFlow0
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
Minimally Supervised Written-to-Spoken Text Normalization0
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task LearningCode0
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
Wav2Letter: an End-to-End ConvNet-based Speech Recognition SystemCode1
Purely sequence-trained neural networks for ASR based on lattice-free MMI0
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms0
On the verbalization patterns of part-whole relations in isiZulu0
Socially-Aware Animated Intelligent Personal Assistant Agent0
Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms0
Task Lineages: Dialog State Tracking for Flexible Interaction0
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications0
Generating sets of related sentences from input seed features0
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
Evaluation of acoustic word embeddings0
CobaltF: A Fluent Metric for MT Evaluation0
Alignment-Based Neural Machine Translation0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features0
Pynini: A Python library for weighted finite-state grammar compilation0
Distributed representation and estimation of WFST-based n-gram models0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search0
Mining linguistic tone patterns with symbolic representation0
Improving cross-domain n-gram language modelling with skipgrams0
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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