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 21012150 of 6433 papers

TitleStatusHype
Blank-regularized CTC for Frame Skipping in Neural Transducer0
A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning0
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark0
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks0
A Lexical-aware Non-autoregressive Transformer-based ASR Model0
Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System0
Use of Speech Impairment Severity for Dysarthric Speech Recognition0
FunASR: A Fundamental End-to-End Speech Recognition Toolkit0
DQ-Whisper: Joint Distillation and Quantization for Efficient Multilingual Speech Recognition0
Boosting Local Spectro-Temporal Features for Speech Analysis0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion0
Application-Agnostic Language Modeling for On-Device ASR0
Critical Appraisal of Artificial Intelligence-Mediated Communication0
OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking0
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations0
Investigating the Sensitivity of Automatic Speech Recognition Systems to Phonetic Variation in L2 Englishes0
Continual Learning for End-to-End ASR by Averaging Domain Experts0
Accelerator-Aware Training for Transducer-Based Speech Recognition0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers0
Quran Recitation Recognition using End-to-End Deep Learning0
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models0
Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes0
Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions0
Multi-Temporal Lip-Audio Memory for Visual Speech Recognition0
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition0
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers0
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst TasksCode0
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks0
End-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders0
Employing Hybrid Deep Neural Networks on Dari Speech0
Considerations for Ethical Speech Recognition Datasets0
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge0
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding0
Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition0
A Review of Deep Learning Techniques for Speech Processing0
Building a Non-native Speech Corpus Featuring Chinese-English Bilingual Children: Compilation and Rationale0
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRCode0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization0
Understanding Shared Speech-Text Representations0
Optimizing Deep Learning Models For Raspberry PiCode0
Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities0
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition0
Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey0
Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding0
Towards the Universal Defense for Query-Based Audio Adversarial Attacks0
OLISIA: a Cascade System for Spoken Dialogue State TrackingCode0
Security and Privacy Problems in Voice Assistant Applications: A Survey0
Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods0
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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