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 52515300 of 6433 papers

TitleStatusHype
Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks0
Speech recognition for medical conversations0
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition0
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition0
Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference0
Supervised and Unsupervised Transfer Learning for Question Answering0
Phonemic and Graphemic Multilingual CTC Based Speech Recognition0
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection0
Analysis of Dropout in Online Learning0
Feed Forward and Backward Run in Deep Convolution Neural Network0
Block-Sparse Recurrent Neural Networks0
Towards Language-Universal End-to-End Speech Recognition0
Improved training for online end-to-end speech recognition systemsCode0
Multilingual Speech Recognition With A Single End-To-End Model0
Robust Speech Recognition Using Generative Adversarial Networks0
Dual Language Models for Code Switched Speech Recognition0
序列標記與配對方法用於語音辨識錯誤偵測及修正 (On the Use of Sequence Labeling and Matching Methods for ASR Error Detection and Correction) [In Chinese]0
Using Teacher-Student Model For Emotional Speech Recognition[In Chinese]0
Unsupervised Method for Improving Arabic Speech Recognition Systems0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags0
Attentive Language Models0
Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks0
Can Discourse Relations be Identified Incrementally?0
Improving Black-box Speech Recognition using Semantic Parsing0
Learning neural trans-dimensional random field language models with noise-contrastive estimation0
Deep word embeddings for visual speech recognitionCode0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning0
A Supervised STDP-based Training Algorithm for Living Neural Networks0
Attention-Based Models for Text-Dependent Speaker VerificationCode0
A Study of All-Convolutional Encoders for Connectionist Temporal Classification0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition0
Rotational Unit of MemoryCode0
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGACode0
Deep Neural Networks0
Trace norm regularization and faster inference for embedded speech recognition RNNsCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System0
Combining Multiple Views for Visual Speech Recognition0
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword SpottingCode0
Embedding-Based Speaker Adaptive Training of Deep Neural Networks0
Adapting general-purpose speech recognition engine output for domain-specific natural language question answering0
Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR0
Contaminated speech training methods for robust DNN-HMM distant speech recognitionCode0
Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures0
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environmentsCode1
Syntactic and Semantic Features For Code-Switching Factored Language Models0
Resolution limits on visual speech recognition0
Which phoneme-to-viseme maps best improve visual-only computer lip-reading?0
Visual speech recognition: aligning terminologies for better understanding0
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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