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 17511800 of 6433 papers

TitleStatusHype
Demonstration of the EmoteWizard of Oz Interface for Empathic Robotic Tutors0
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems0
DENOASR: Debiasing ASRs through Selective Denoising0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition0
Densely Connected Convolutional Networks for Speech Recognition0
Dense Multimodal Fusion for Hierarchically Joint Representation0
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition0
Clean Label Attacks against SLU Systems0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers0
Are disentangled representations all you need to build speaker anonymization systems?0
Dereverberation of Autoregressive Envelopes for Far-field Speech Recognition0
Describing Multimedia Content using Attention-based Encoder--Decoder Networks0
Design and development a children's speech database0
Design and Development of Speech Corpora for Air Traffic Control Training0
Design and Optimization of a Speech Recognition Front-End for Distant-Talking Control of a Music Playback Device0
Designing an Evaluation Framework for Spoken Term Detection and Spoken Document Retrieval at the NTCIR-9 SpokenDoc Task0
Class LM and word mapping for contextual biasing in End-to-End ASR0
Designing Language Technology Applications: A Wizard of Oz Driven Prototyping Framework0
Designing the Latvian Speech Recognition Corpus0
Designing the Next Generation of Intelligent Personal Robotic Assistants for the Physically Impaired0
Design of a novel Korean learning application for efficient pronunciation correction0
Design of a Tigrinya Language Speech Corpus for Speech Recognition0
Detecting Adversarial Attacks On Audiovisual Speech Recognition0
Automatic Enhancement of LTAG Treebank0
Automatic Estimation of Intelligibility Measure for Consonants in Speech0
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty0
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.00
Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions0
Detecting Health Related Discussions in Everyday Telephone Conversations for Studying Medical Events in the Lives of Older Adults0
Detecting Institutional Dialog Acts in Police Traffic Stops0
Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts0
Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memory0
Detecting Retries of Voice Search Queries0
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model0
Detection of Acoustic-Phonetic Landmarks in Mismatched Conditions using a Biomimetic Model of Human Auditory Processing0
Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition0
Classist Tools: Social Class Correlates with Performance in NLP0
Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder0
Developing a Speech Recognition System for Recognizing Tonal Speech Signals Using a Convolutional Neural Network0
Developing ASR for Indonesian-English Bilingual Language Teaching0
A Recorded Debating Dataset0
Developing further speech recognition resources for Welsh0
Developing language technology tools and resources for a resource-poor language: Sindhi0
Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset0
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability0
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features0
Development and evaluation of a deep learning algorithm for German word recognition from lip movements0
Development and Evaluation of Speech Synthesis Corpora for Latvian0
Development and Evaluation of Speech Recognition for the Welsh Language0
Adversarial Training for Multilingual Acoustic Modeling0
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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