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 10011050 of 6433 papers

TitleStatusHype
Evaluation Phonemic Transcription of Low-Resource Tonal Languages for Language DocumentationCode0
Spanish and English Phoneme Recognition by Training on Simulated Classroom Audio Recordings of Collaborative Learning EnvironmentsCode0
Explainability of Speech Recognition Transformers via Gradient-based Attention VisualizationCode0
Exploring neural oscillations during speech perception via surrogate gradient spiking neural networksCode0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Evaluating robustness of You Only Hear Once(YOHO) Algorithm on noisy audios in the VOICe DatasetCode0
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from SpeechCode0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst TasksCode0
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech ToolkitCode0
A Framework for Adapting Human-Robot Interaction to Diverse User GroupsCode0
Speech Recognition Challenge in the Wild: Arabic MGB-3Code0
Error-preserving Automatic Speech Recognition of Young English Learners' LanguageCode0
Evaluating context-invariance in unsupervised speech representationsCode0
Evaluation of End-to-End Continuous Spanish Lipreading in Different Data ConditionsCode0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-trainingCode0
Word-level Embeddings for Cross-Task Transfer Learning in Speech ProcessingCode0
End-to-End Speech Recognition With Joint Dereverberation Of Sub-Band Autoregressive EnvelopesCode0
Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation NetworkCode0
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the ArtCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Enhancing Quantised End-to-End ASR Models via PersonalisationCode0
AequeVox: Automated Fairness Testing of Speech Recognition SystemsCode0
End-to-End Speech Recognition From the Raw WaveformCode0
End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model PretrainingCode0
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based ModelCode0
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix AugmentationCode0
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification TasksCode0
FastEmit: Low-latency Streaming ASR with Sequence-level Emission RegularizationCode0
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical StudyCode0
End-to-end Audiovisual Speech RecognitionCode0
End to End ASR System with Automatic Punctuation InsertionCode0
End-to-End Attention-based Large Vocabulary Speech RecognitionCode0
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern ArchitecturesCode0
End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic HandsCode0
Adversarial Training For Low-Resource Disfluency CorrectionCode0
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language ModelsCode0
Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech dataCode0
Arabic Speech Recognition by End-to-End, Modular Systems and HumanCode0
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based AugmentationCode0
3D Convolutional Neural Networks for Cross Audio-Visual Matching RecognitionCode0
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda NetworksCode0
Emotional Speech Recognition with Pre-trained Deep Visual ModelsCode0
Efficient Adaptation of Multilingual Models for Japanese ASRCode0
Efficient and Generic 1D Dilated Convolution Layer for Deep LearningCode0
Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech TasksCode0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced LanguagesCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
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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