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 58015850 of 6433 papers

TitleStatusHype
AI4D -- African Language ProgramCode0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
Perceptual and Task-Oriented Assessment of a Semantic Metric for ASR EvaluationCode0
fairseq S2T: Fast Speech-to-Text Modeling with fairseqCode0
Extended Bit-Plane Compression for Convolutional Neural Network AcceleratorsCode0
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)Code0
Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition SystemsCode0
A Small and Fast BERT for Chinese Medical Punctuation RestorationCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
A Transformer with Interleaved Self-attention and Convolution for Hybrid Acoustic ModelsCode0
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric SpeechCode0
Exploring spectro-temporal features in end-to-end convolutional neural networksCode0
Efficient Adaptation of Multilingual Models for Japanese ASRCode0
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN FeaturesCode0
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech RecognitionCode0
A Gentle Tutorial of Recurrent Neural Network with Error BackpropagationCode0
Exploring neural oscillations during speech perception via surrogate gradient spiking neural networksCode0
Exploring Generative Error Correction for Dysarthric Speech RecognitionCode0
Iterative Pseudo-Labeling for Speech RecognitionCode0
Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptationCode0
Twin Networks: Matching the Future for Sequence GenerationCode0
Robustness Analysis of Deep Learning Frameworks on Mobile PlatformsCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Twin Regularization for online speech recognitionCode0
Key Frame Mechanism For Efficient Conformer Based End-to-end Speech RecognitionCode0
A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser Ney SmoothingCode0
Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit PoetryCode0
Keyphrase Cloud Generation of Broadcast NewsCode0
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech RecognitionCode0
Automating Feedback Analysis in Surgical Training: Detection, Categorization, and AssessmentCode0
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation SystemCode0
Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack DetectionCode0
Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired ListenersCode0
Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic InformationCode0
Investigating the Emergent Audio Classification Ability of ASR Foundation ModelsCode0
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event LocalizationCode0
Speaker-adaptive Lip Reading with User-dependent PaddingCode0
Automatic Speech Recognition and Query By Example for Creole Languages DocumentationCode0
Boosting Cross-Domain Speech Recognition with Self-SupervisionCode0
Exploiting Adapters for Cross-lingual Low-resource Speech RecognitionCode0
Muddling Label Regularization: Deep Learning for Tabular DatasetsCode0
Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition EncoderCode0
Explaining Spectrograms in Machine Learning: A Study on Neural Networks for Speech ClassificationCode0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User InterfacesCode0
Two-Pass End-to-End Speech RecognitionCode0
The OCON model: an old but gold solution for distributable supervised classificationCode0
DELTA: A DEep learning based Language Technology plAtformCode0
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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