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 2701–2750 of 6433 papers

TitleStatusHype
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
Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition—0
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT—0
Granary: Speech Recognition and Translation Dataset in 25 European Languages—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
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network—0
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding—0
Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining—0
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition—0
GRASS: the Graz corpus of Read And Spontaneous Speech—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
Grouping Language Model Boundary Words to Speed K--Best Extraction from Hypergraphs—0
Grow and Prune Compact, Fast, and Accurate LSTMs—0
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks—0
Guided contrastive self-supervised pre-training for automatic speech recognition—0
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces—0
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
CHISPA on the GO: A mobile Chinese-Spanish translation service for travellers in trouble—0
Arabic Dialect Processing Tutorial—0
Hallucination of speech recognition errors with sequence to sequence learning—0
Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models—0
Halving transcription time: A fast, user-friendly and GDPR-compliant workflow to create AI-assisted transcripts for content analysis—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Handwriting recognition for Scottish Gaelic—0
A cost minimization approach to fix the vocabulary size in a tokenizer for an End-to-End ASR system—0
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition—0
Accented Speech Recognition Inspired by Human Perception—0
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm—0
Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference—0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning—0
Fast offline Transformer-based end-to-end automatic speech recognition for real-world applications—0
Chinese Medical Speech Recognition with Punctuated Hypothesis—0
Arabic Diacritization with Recurrent Neural Networks—0
Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects—0
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition—0
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing—0
Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition—0
Head-synchronous Decoding for Transformer-based Streaming ASR—0
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Hearings and mishearings: decrypting the spoken word—0
Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides—0
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Fast Labeling and Transcription with the Speechalyzer Toolkit—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