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

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Showing 751–800 of 6433 papers

TitleStatusHype
Dynamic Data Pruning for Automatic Speech Recognition—0
ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMsCode1
FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech DataCode0
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNetCode1
Sequential Editing for Lifelong Training of Speech Recognition Models—0
MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization—0
Towards Building an End-to-End Multilingual Automatic Lyrics Transcription ModelCode1
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR—0
Investigating Confidence Estimation Measures for Speaker Diarization—0
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024—0
Decoder-only Architecture for Streaming End-to-end Speech Recognition—0
Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss—0
Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment—0
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices—0
InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions—0
Perception of Phonological Assimilation by Neural Speech Recognition Models—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries—0
DASB -- Discrete Audio and Speech Benchmark—0
Joint vs Sequential Speaker-Role Detection and Automatic Speech Recognition for Air-traffic Control—0
Children's Speech Recognition through Discrete Token Enhancement—0
ManWav: The First Manchu ASR Model—0
Rapid Language Adaptation for Multilingual E2E Speech Recognition Using Encoder Prompting—0
Transcribe, Align and Segment: Creating speech datasets for low-resource languages—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Performant ASR Models for Medical Entities in Accented Speech—0
Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of SpeechCode0
Unsupervised Online Continual Learning for Automatic Speech RecognitionCode0
SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token SynchronizationCode2
Self-Train Before You TranscribeCode0
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and RefinementCode3
Automatic Speech Recognition for Biomedical Data in Bengali Language—0
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
Large Language Models for Dysfluency Detection in Stuttered Speech—0
Optimized Speculative Sampling for GPU Hardware AcceleratorsCode0
Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition—0
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving—0
Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge—0
Optimizing Byte-level Representation for End-to-end ASR—0
On the Evaluation of Speech Foundation Models for Spoken Language Understanding—0
An efficient text augmentation approach for contextualized Mandarin speech recognition—0
Simul-Whisper: Attention-Guided Streaming Whisper with Truncation DetectionCode2
Whisper-Flamingo: Integrating Visual Features into Whisper for Audio-Visual Speech Recognition and TranslationCode3
Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition—0
Learning Language Structures through Grounding—0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified