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 24512500 of 6433 papers

TitleStatusHype
End-to-End Multi-Person Audio/Visual Automatic 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
Deep Learning Enabled Semantic Communications with Speech Recognition and SynthesisCode1
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition0
Vietnamese Automatic Speech Recognition using Wav2vec 2.0Code1
Wav2vec2 Base Vietnamese 160hCode1
Online Model Compression for Federated Learning with Large Models0
Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation AssessmentCode1
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy0
Hearing voices at the National Library -- a speech corpus and acoustic model for the Swedish language0
A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition0
Speaker Recognition in the WildCode1
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
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo LanguagesCode1
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network0
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM0
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool0
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 20220
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario0
JHU IWSLT 2022 Dialect Speech Translation System Description0
Multimodal fusion via cortical network inspired losses0
Automatic Speech Recognition and Query By Example for Creole Languages DocumentationCode0
SSNCSE_NLP@LT-EDI-ACL2022: Speech Recognition for Vulnerable Individuals in Tamil using pre-trained XLSR models0
Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists0
MTL-SLT: Multi-Task Learning for Spoken Language Tasks0
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation0
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil0
CMU’s IWSLT 2022 Dialect Speech Translation System0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 20220
SUH_ASR@LT-EDI-ACL2022: Transformer based Approach for Speech Recognition for Vulnerable Individuals in Tamil0
Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)0
How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia DetectionCode0
Enhancing Documentation of Hupa with Automatic Speech Recognition0
Bilingual End-to-End ASR with Byte-Level Subwords0
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?0
Mask scalar prediction for improving robust automatic speech recognition0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers0
Supervised Attention in Sequence-to-Sequence Models for Speech Recognition0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications0
Improved far-field speech recognition using Joint Variational Autoencoder0
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment0
Efficient Training of Neural Transducer for Speech Recognition0
Deep transfer operator learning for partial differential equations under conditional shiftCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems0
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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