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 57515800 of 6433 papers

TitleStatusHype
Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks0
Text Generation with Speech Synthesis for ASR Data Augmentation0
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models0
Text Injection for Neural Contextual Biasing0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis0
Text Normalization Infrastructure that Scales to Hundreds of Language Varieties0
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator0
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation0
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition0
Textual Echo Cancellation0
Textual Inference and Meaning Representation in Human Robot Interaction0
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition0
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages0
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media0
The 2019 BBN Cross-lingual Information Retrieval System0
The acquisition and dialog act labeling of the EDECAN-SPORTS corpus0
The AFRL IWSLT 2018 Systems: What Worked, What Didn’t0
The AFRL IWSLT 2020 Systems: Work-From-Home Edition0
The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence0
The Ambiguous World of Emotion Representation0
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling0
The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers0
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results0
The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions0
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese0
The CAPIO 2017 Conversational Speech Recognition System0
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System0
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios0
The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization0
The coding and annotation of multimodal dialogue acts0
The Cohort and Speechify Libraries for Rapid Construction of Speech Enabled Applications for Android0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge0
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning0
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task LearningCode0
Consistent Transcription and Translation of SpeechCode0
Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource LanguagesCode0
On the Choice of Modeling Unit for Sequence-to-Sequence Speech RecognitionCode0
An Overview of Multi-Task Learning in Deep Neural NetworksCode0
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural NetworksCode0
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video TranscriptCode0
Attentional Speech Recognition Models Misbehave on Out-of-domain UtterancesCode0
Robust Audio Adversarial Example for a Physical AttackCode0
Spanish and English Phoneme Recognition by Training on Simulated Classroom Audio Recordings of Collaborative Learning EnvironmentsCode0
Efficient and Generic 1D Dilated Convolution Layer for Deep LearningCode0
Joint Automatic Speech Recognition And Structure Learning For Better Speech UnderstandingCode0
Momentum Pseudo-Labeling for Semi-Supervised Speech RecognitionCode0
Jasper: An End-to-End Convolutional Neural Acoustic ModelCode0
AI-Generated Song Detection via Lyrics TranscriptsCode0
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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