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 16011650 of 6433 papers

TitleStatusHype
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation0
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network0
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance0
Ambient Search: A Document Retrieval System for Speech Streams0
Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication0
Audio De-identification - a New Entity Recognition Task0
Audio De-identification: A New Entity Recognition Task0
A Mandarin-English Code-Switching Corpus0
Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting0
Audio-conditioned phonemic and prosodic annotation for building text-to-speech models from unlabeled speech data0
Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses0
A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages0
A 71.2-μW Speech Recognition Accelerator with Recurrent Spiking Neural Network0
Audio-attention discriminative language model for ASR rescoring0
Audio Attacks and Defenses against AED Systems -- A Practical Study0
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition0
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning0
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition0
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement0
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition0
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition0
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition0
Data Selection With Fewer Words0
Data-selective Transfer Learning for Multi-Domain Speech Recognition0
Data centric approach to Chinese Medical Speech Recognition0
Data Techniques For Online End-to-end Speech Recognition0
Database Meets Deep Learning: Challenges and Opportunities0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions0
DCIM-AVSR : Efficient Audio-Visual Speech Recognition via Dual Conformer Interaction Module0
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR0
Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test0
DECCA Repurposed: Detecting transcription inconsistencies without an orthographic standard0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples0
A Local Detection Approach for Named Entity Recognition and Mention Detection0
Decipherment0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer0
DECODA: a call-centre human-human spoken conversation corpus0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition0
Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty0
Decoder-only Architecture for Streaming End-to-end Speech Recognition0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios0
Decoding visemes: improving machine lipreading0
Decoding with Finite-State Transducers on GPUs0
Decoupled Federated Learning for ASR with Non-IID Data0
Atypical Inputs in Educational Applications0
Almost Unsupervised Text to Speech and Automatic Speech Recognition0
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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