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 61516200 of 6433 papers

TitleStatusHype
Challenges and Opportunities of Speech Recognition for Bengali Language0
Challenges and Solutions for Consistent Annotation of Vietnamese Treebank0
Challenges in Speech Recognition and Translation of High-Value Low-Density Polysynthetic Languages0
Challenges of Applying Automatic Speech Recognition for Transcribing EU Parliament Committee Meetings: A Pilot Study0
Challenges of Computational Processing of Code-Switching0
Challenging the Boundaries of Speech Recognition: The MALACH Corpus0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech0
CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi0
Chaotic Variational Auto encoder-based Adversarial Machine Learning0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction0
Character-Aware Attention-Based End-to-End Speech Recognition0
Character-aware audio-visual subtitling in context0
Characterizing Audio Adversarial Examples Using Temporal Dependency0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement0
Characterizing the Weight Space for Different Learning Models0
Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition0
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks0
Children's Speech Recognition through Discrete Token Enhancement0
Child Speech Recognition in Human-Robot Interaction: Problem Solved?0
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings0
Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides0
Chinese Medical Speech Recognition with Punctuated Hypothesis0
Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference0
CHISPA on the GO: A mobile Chinese-Spanish translation service for travellers in trouble0
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition0
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge0
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning0
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition0
Class-Based Language Modeling for Translating into Morphologically Rich Languages0
Class-based LSTM Russian Language Model with Linguistic Information0
Classification Error Bound for Low Bayes Error Conditions in Machine Learning0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines0
Classifying Arab Names Geographically0
Classifying Dialogue Acts in Multi-party Live Chats0
Classist Tools: Social Class Correlates with Performance in NLP0
Class LM and word mapping for contextual biasing in End-to-End ASR0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers0
Clean Label Attacks against SLU Systems0
Click or Type: An Analysis of Wizard's Interaction for Future Wizard Interface Design0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings0
Clinical Dialogue Transcription Error Correction using Seq2Seq Models0
Clipping free attacks against artificial neural networks0
Clipping Free Attacks Against Neural Networks0
Closing the Gap between Single-User and Multi-User VoiceFilter-Lite0
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions0
Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages0
Cloud-Based Face and Speech Recognition for Access Control Applications0
Community Detection Clustering via Gumbel Softmax0
CMU’s IWSLT 2022 Dialect Speech Translation System0
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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