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 2651–2700 of 6433 papers

TitleStatusHype
Class-Based Language Modeling for Translating into Morphologically Rich Languages—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Feature selection using Fisher's ratio technique for automatic speech recognition—0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition—0
Generating More Specific Questions for Acquiring Attributes of Unknown Concepts from Users—0
Generating Robust Audio Adversarial Examples using Iterative Proportional Clipping—0
Generating sets of related sentences from input seed features—0
A Random Gossip BMUF Process for Neural Language Modeling—0
Generating Synthetic Clinical Speech Data through Simulated ASR Deletion Error—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Generation and Pruning of Pronunciation Variants to Improve ASR Accuracy—0
G\'en\'eration des prononciations de noms propres \`a l'aide des Champs Al\'eatoires Conditionnels (Pronunciation generation for proper names using Conditional Random Fields) [in French]—0
On Architectures and Training for Raw Waveform Feature Extraction in ASR—0
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement—0
Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges—0
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition—0
Generative Context-aware Fine-tuning of Self-supervised Speech Models—0
Feature Normalisation for Robust Speech Recognition—0
Generative Goal-Driven User Simulation for Dialog Management—0
Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition—0
Generative linguistic representation for spoken language identification—0
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning—0
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting—0
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Geometric Understanding of Deep Learning—0
German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis—0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders—0
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition—0
GhostVec: A New Threat to Speaker Privacy of End-to-End Speech Recognition System—0
Gibbs Sampling with Low-Power Spiking Digital Neurons—0
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults—0
Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts—0
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge—0
Globally Normalising the Transducer for Streaming Speech Recognition—0
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network—0
Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining—0
Global SNR Estimation of Speech Signals using Entropy and Uncertainty Estimates from Dropout Networks—0
GNCformer Enhanced Self-attention for Automatic Speech Recognition—0
Goal-driven text descriptions for images—0
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks—0
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces—0
CHISPA on the GO: A mobile Chinese-Spanish translation service for travellers in trouble—0
Arabic Dialect Processing Tutorial—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
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord 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
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage 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
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord 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