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 57015750 of 6433 papers

TitleStatusHype
A Review of Speaker Diarization: Recent Advances with Deep Learning0
A Robotic Agent in a Virtual Environment that Performs Situated Incremental Understanding of Navigational Utterances0
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech0
Articulatory Features for ASR of Pathological Speech0
Articulatory information and Multiview Features for Large Vocabulary Continuous Speech Recognition0
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications0
Artificial Neural Networks to Recognize Speakers Division from Continuous Bengali Speech0
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English0
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection0
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition0
AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition0
A Scalable Architecture For Web Deployment of Spoken Dialogue Systems0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding0
A Self Learning Vocal Interface for Speech-impaired Users0
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech0
A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking0
A Semi-autonomous System for Creating a Human-Machine Interaction Corpus in Virtual Reality: Application to the ACORFORMed System for Training Doctors to Break Bad News0
A Short Survey on Data Clustering Algorithms0
A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations0
A simple and effective postprocessing method for image classification0
A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information0
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data0
A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations0
Ask2Mask: Guided Data Selection for Masked Speech Modeling0
A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility0
A Speech Test Set of Practice Business Presentations with Additional Relevant Texts0
A spelling correction model for end-to-end speech recognition0
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals0
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling0
Automatic Speech Recognition Advancements for Indigenous Languages of the Americas0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition0
ASR-Aware End-to-end Neural Diarization0
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach0
ASR Bundestag: A Large-Scale political debate dataset in German0
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction0
ASR Error Correction and Domain Adaptation Using Machine Translation0
ASR Error Correction using Large Language Models0
ASR Error Detection via Audio-Transcript entailment0
ASR error management for improving spoken language understanding0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems0
ASR for Documenting Acutely Under-Resourced Indigenous Languages0
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding0
ASR in German: A Detailed Error Analysis0
ASR is all you need: cross-modal distillation for lip reading0
ASR Rescoring and Confidence Estimation with ELECTRA0
Assessing ASR Model Quality on Disordered Speech using BERTScore0
Assessing Relative Sentence Complexity using an Incremental CCG Parser0
Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows0
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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