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 13011350 of 6433 papers

TitleStatusHype
Segment-Level Vectorized Beam Search Based on Partially Autoregressive Inference0
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data0
Connecting Speech Encoder and Large Language Model for ASR0
Unsupervised Accent Adaptation Through Masked Language Model Correction Of Discrete Self-Supervised Speech Units0
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''0
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available DataCode1
Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation LearningCode1
On the Relation between Internal Language Model and Sequence Discriminative Training for Neural Transducers0
Human Transcription Quality ImprovementCode0
Cross-modal Alignment with Optimal Transport for CTC-based ASR0
Speech enhancement with frequency domain auto-regressive modeling0
My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech0
NTT speaker diarization system for CHiME-7: multi-domain, multi-microphone End-to-end and vector clustering diarization0
Affect Recognition in Conversations Using Large Language Models0
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR0
Massive End-to-end Models for Short Search Queries0
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
Memory-augmented conformer for improved end-to-end long-form ASRCode1
A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement0
Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling0
CoMFLP: Correlation Measure based Fast Search on ASR Layer PruningCode0
Bridging the Gaps of Both Modality and Language: Synchronous Bilingual CTC for Speech Translation and Speech RecognitionCode1
Sparsely Shared LoRA on Whisper for Child Speech Recognition0
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition0
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition0
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
Exploring Speech Enhancement for Low-resource Speech Synthesis0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition0
Semi-Autoregressive Streaming ASR With Label Context0
Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition0
End-to-End Speech Recognition Contextualization with Large Language Models0
HTEC: Human Transcription Error Correction0
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models0
Training dynamic models using early exits for automatic speech recognition on resource-constrained devicesCode0
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter0
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation0
Instruction-Following Speech Recognition0
Investigating End-to-End ASR Architectures for Long Form Audio Transcription0
HypR: A comprehensive study for ASR hypothesis revising with a reference corpusCode1
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting0
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning0
Enhancing Quantised End-to-End ASR Models via PersonalisationCode0
Improving Speech Recognition for African American English With Audio Classification0
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation0
Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints0
Transformer Based Punctuation Restoration for TurkishCode0
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction0
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability0
Show:102550
← PrevPage 27 of 129Next →

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