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 2351–2400 of 6433 papers

TitleStatusHype
The ZevoMOS entry to VoiceMOS Challenge 2022—0
Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech—0
Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition—0
Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project—0
Residual Language Model for End-to-end Speech Recognition—0
Toward Zero Oracle Word Error Rate on the Switchboard Benchmark—0
Learning-Based Data Storage [Vision] (Technical Report)—0
Investigation of Ensemble features of Self-Supervised Pretrained Models for Automatic Speech Recognition—0
AHD ConvNet for Speech Emotion Classification—0
Training Neural Networks using SAT solvers—0
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos—0
Joint Encoder-Decoder Self-Supervised Pre-training for ASR—0
Context-based out-of-vocabulary word recovery for ASR systems in Indian languages—0
Revisiting End-to-End Speech-to-Text Translation From Scratch—0
A New Frontier of AI: On-Device AI Training and PersonalizationCode2
LegoNN: Building Modular Encoder-Decoder Models—0
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker RecognitionCode1
FedNST: Federated Noisy Student Training for Automatic Speech Recognition—0
Lip-Listening: Mixing Senses to Understand Lips using Cross Modality Knowledge Distillation for Word-Based Models—0
Variable-rate hierarchical CPC leads to acoustic unit discovery in speechCode1
LAE: Language-Aware Encoder for Monolingual and Multilingual ASRCode1
Squeezeformer: An Efficient Transformer for Automatic Speech RecognitionCode2
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations—0
Automatic Speech Recognition for Irish: the ABAIR-ÉIST System—0
Development and Evaluation of Speech Recognition for the Welsh Language—0
Developing Automatic Speech Recognition for Scottish Gaelic—0
LiSTra Automatic Speech Translation: English to Lingala Case Study—0
Handwriting recognition for Scottish Gaelic—0
SSR7000: A Synchronized Corpus of Ultrasound Tongue Imaging for End-to-End Silent Speech RecognitionCode0
Generating Synthetic Clinical Speech Data through Simulated ASR Deletion Error—0
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition—0
A Systematic Approach to Derive a Refined Speech Corpus for Sinhala—0
Conversational Speech Recognition Needs Data? Experiments with Austrian German—0
Multiword Expressions and the Low-Resource Scenario from the Perspective of a Local Oral Culture—0
Post-Stroke Speech Transcription Challenge (Task B): Correctness Detection in Anomia Diagnosis with Imperfect Transcripts—0
Multilingual Transfer Learning for Children Automatic Speech Recognition—0
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut—0
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command RecognitionCode1
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection—0
Towards a Unified ASR System for the Armenian Standards—0
Evaluation of Off-the-shelf Speech Recognizers on Different Accents in a Dialogue Domain—0
Building Open-source Speech Technology for Low-resource Minority Languages with SáMi as an Example – Tools, Methods and Experiments—0
Standard German Subtitling of Swiss German TV content: the PASSAGE Project—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
Samrómur: Crowd-sourcing large amounts of data—0
ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions—0
ParlaSpeech-HR - a Freely Available ASR Dataset for Croatian Bootstrapped from the ParlaMint Corpus—0
DiaBiz – an Annotated Corpus of Polish Call Center Dialogs—0
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori—0
Samrómur Children: An Icelandic Speech Corpus—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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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