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 49515000 of 6433 papers

TitleStatusHype
Using a Serious Game to Collect a Child Learner Speech Corpus0
Using Automatic Speech Recognition in Spoken Corpus Curation0
Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems0
Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition0
Using Discourse Information for Education with a Spanish-Chinese Parallel Corpus0
Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences0
Using English Acoustic Models for Hindi Automatic Speech Recognition0
Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition0
Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition0
Using Kaldi for Automatic Speech Recognition of Conversational Austrian German0
Using Large Language Model for End-to-End Chinese ASR and NER0
Using multiple ASR hypotheses to boost i18n NLU performance0
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models0
Using Neural Networks for Modeling and Representing Natural Languages0
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks0
Using of heterogeneous corpora for training of an ASR system0
Using Ontology-based Approaches to Representing Speech Transcripts for Automated Speech Scoring0
Using Out-of-Domain Data for Lexical Addressee Detection in Human-Human-Computer Dialog0
Using Related Languages to Enhance Statistical Language Models0
Using Spoken Word Posterior Features in Neural Machine Translation0
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian0
Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems0
Using Teacher-Student Model For Emotional Speech Recognition[In Chinese]0
Using Text Injection to Improve Recognition of Personal Identifiers in Speech0
Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation0
Using Tone Information in Thai Spelling Speech Recognition0
Using Topological Framework for the Design of Activation Function and Model Pruning in Deep Neural Networks0
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models0
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder0
Utilizing constituent structure for compound analysis0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders0
Utterance-level neural confidence measure for end-to-end children speech recognition0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones0
UWSpeech: Speech to Speech Translation for Unwritten Languages0
V2S attack: building DNN-based voice conversion from automatic speaker verification0
VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording0
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition0
Vaidya: A Spoken Dialog System for Health Domain0
VAIS ASR: Building a conversational speech recognition system using language model combination0
VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages0
VALLR: Visual ASR Language Model for Lip Reading0
ValSub: Subsampling Validation Data to Mitigate Forgetting during ASR Personalization0
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition0
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition0
Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition0
Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling0
VAST: A Corpus of Video Annotation for Speech Technologies0
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning0
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference0
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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