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 57515800 of 6433 papers

TitleStatusHype
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition0
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition0
A Tutorial on Deep Neural Networks for Intelligent Systems0
Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices0
Neural network based spectral mask estimation for acoustic beamformingCode0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition0
Personalized Speech recognition on mobile devices0
Segmental Recurrent Neural Networks for End-to-end Speech Recognition0
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection0
The IBM 2016 Speaker Recognition System0
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation0
Deep Learning on FPGAs: Past, Present, and Future0
Lipreading with Long Short-Term Memory0
Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)0
Character-Level Incremental Speech Recognition with Recurrent Neural NetworksCode0
Automatic recognition of element classes and boundaries in the birdsong with variable sequences0
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition0
Manifold-Kernels Comparison in MKPLS for Visual Speech Recognition0
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model0
Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation0
Using Filter Banks in Convolutional Neural Networks for Texture ClassificationCode0
Evaluating the Performance of a Speech Recognition based System0
Environmental Noise Embeddings for Robust Speech Recognition0
Minimally Supervised Number Normalization0
Sparse Non-negative Matrix Language Modeling0
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency0
Statistical and Computational Guarantees for the Baum-Welch Algorithm0
Recent Advances in Convolutional Neural Networks0
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media0
Can Pretrained Neural Networks Detect Anatomy?0
Strategies for Training Large Vocabulary Neural Language ModelsCode0
Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition0
Open Source German Distant Speech Recognition: Corpus and Acoustic ModelCode0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey0
Deep Learning for Single and Multi-Session i-Vector Speaker Recognition0
THCHS-30 : A Free Chinese Speech CorpusCode0
調變頻譜分解技術於強健語音辨識之研究 (Investigating Modulation Spectrum Factorization Techniques for Robust Speech Recognition) [In Chinese]0
Development of Speech corpora for different Speech Recognition tasks in Malayalam language0
Listening With Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines0
Calibrated Structured PredictionCode0
Isolated Word Recognition System for Malayalam using Machine Learning0
A Short Survey on Data Clustering Algorithms0
Spoken Language Translation for Polish0
Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification0
Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition0
Blending LSTMs into CNNs0
Task Loss Estimation for Sequence PredictionCode0
Transfer Learning for Speech and Language Processing0
Enhancements in statistical spoken language translation by de-normalization of ASR results0
Learning to retrieve out-of-vocabulary words in speech recognition0
Show:102550
← PrevPage 116 of 129Next →

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