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 27512800 of 6433 papers

TitleStatusHype
Insights on Neural Representations for End-to-End Speech Recognition0
Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator0
Deploying self-supervised learning in the wild for hybrid automatic speech recognition0
Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments0
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data0
Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing0
Unified Modeling of Multi-Domain Multi-Device ASR Systems0
Who Are We Talking About? Handling Person Names in Speech Translation0
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition0
A Closer Look at Audio-Visual Multi-Person Speech Recognition and Active Speaker Selection0
End-to-End Multi-Person Audio/Visual Automatic Speech Recognition0
Improved Meta Learning for Low Resource Speech Recognition0
Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech0
Best of Both Worlds: Multi-task Audio-Visual Automatic Speech Recognition and Active Speaker Detection0
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition0
A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition0
Online Model Compression for Federated Learning with Large Models0
Hearing voices at the National Library -- a speech corpus and acoustic model for the Swedish language0
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy0
Design of a novel Korean learning application for efficient pronunciation correction0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks0
On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training0
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network0
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM0
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition0
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool0
Automatic Speech Recognition and Query By Example for Creole Languages DocumentationCode0
JHU IWSLT 2022 Dialect Speech Translation System Description0
Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists0
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 20220
SSNCSE_NLP@LT-EDI-ACL2022: Speech Recognition for Vulnerable Individuals in Tamil using pre-trained XLSR models0
CMU’s IWSLT 2022 Dialect Speech Translation System0
Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)0
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil0
Multimodal fusion via cortical network inspired losses0
MTL-SLT: Multi-Task Learning for Spoken Language Tasks0
How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia DetectionCode0
SUH_ASR@LT-EDI-ACL2022: Transformer based Approach for Speech Recognition for Vulnerable Individuals in Tamil0
Enhancing Documentation of Hupa with Automatic Speech Recognition0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 20220
Bilingual End-to-End ASR with Byte-Level Subwords0
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation0
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?0
Mask scalar prediction for improving robust automatic speech recognition0
Supervised Attention in Sequence-to-Sequence Models for Speech Recognition0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications0
Improved far-field speech recognition using Joint Variational Autoencoder0
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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