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 12011250 of 6433 papers

TitleStatusHype
Acoustic-to-articulatory Speech Inversion with Multi-task Learning0
4-bit Quantization of LSTM-based Speech Recognition Models0
Collaborative Training of Acoustic Encoders for Speech Recognition0
Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus0
Combining Multiple Views for Visual Speech Recognition0
ASR for Documenting Acutely Under-Resourced Indigenous Languages0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems0
A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition0
ASR error management for improving spoken language understanding0
ASR Error Detection via Audio-Transcript entailment0
A higher order Minkowski loss for improved prediction ability of acoustic model in ASR0
Acoustic to Articulatory Inversion of Speech; Data Driven Approaches, Challenges, Applications, and Future Scope0
ASR Error Correction using Large Language Models0
A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews0
ASR Error Correction and Domain Adaptation Using Machine Translation0
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction0
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR0
A Challenge Set for Advancing Language Modeling0
ASR Bundestag: A Large-Scale political debate dataset in German0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach0
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning0
A hierarchical approach with feature selection for emotion recognition from speech0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information0
ASR-Aware End-to-end Neural Diarization0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition0
AHD ConvNet for Speech Emotion Classification0
Automatic Speech Recognition Advancements for Indigenous Languages of the Americas0
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling0
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding0
Accurate synthesis of Dysarthric Speech for ASR data augmentation0
Co-learning synaptic delays, weights and adaptation in spiking neural networks0
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals0
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA0
A spelling correction model for end-to-end speech recognition0
A Speech Test Set of Practice Business Presentations with Additional Relevant Texts0
A GrAF-compliant Indonesian Speech Recognition Web Service on the Language Grid for Transcription Crowdsourcing0
Acoustic, Phonetic and Prosodic Features of Parkinson's disease Speech0
A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation0
Improving noisy student training for low-resource languages in End-to-End ASR using CycleGAN and inter-domain losses0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning0
Ask2Mask: Guided Data Selection for Masked Speech Modeling0
Accurate and Structured Pruning for Efficient Automatic Speech Recognition0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition0
A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking0
A Generative Model of a Pronunciation Lexicon for Hindi0
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition0
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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