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 41014150 of 6433 papers

TitleStatusHype
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician0
Challenges of Applying Automatic Speech Recognition for Transcribing EU Parliament Committee Meetings: A Pilot Study0
Speech Transcription Challenges for Resource Constrained Indigenous Language Cree0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval0
LinTO Platform: A Smart Open Voice Assistant for Business Environments0
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic0
Fully Convolutional ASR for Less-Resourced Endangered Languages0
The 2019 BBN Cross-lingual Information Retrieval System0
Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages0
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition0
Gender Detection from Human Voice Using Tensor Analysis0
Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition0
Style Variation as a Vantage Point for Code-Switching0
Multi-head Monotonic Chunkwise Attention For Online Speech Recognition0
Learning to Rank Intents in Voice Assistants0
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applicationsCode1
Multiresolution and Multimodal Speech Recognition with Transformers0
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTubeCode0
Meta-Transfer Learning for Code-Switched Speech RecognitionCode1
Neural Speech Separation Using Spatially Distributed Microphones0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise0
Research on Modeling Units of Transformer Transducer for Mandarin Speech Recognition0
Jointly Trained Transformers models for Spoken Language Translation0
Adversarial Machine Learning in Network Intrusion Detection Systems0
Cloud-Based Face and Speech Recognition for Access Control Applications0
End-to-end speech-to-dialog-act recognition0
Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party TranscriptionCode0
A Study of Non-autoregressive Model for Sequence Generation0
Curriculum Pre-training for End-to-End Speech Translation0
ESPnet-ST: All-in-One Speech Translation Toolkit0
Language-agnostic Multilingual Modeling0
End-to-End Whisper to Natural Speech Conversion using Modified Transformer Network0
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact CentersCode1
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings0
How to Teach DNNs to Pay Attention to the Visual Modality in Speech RecognitionCode1
AlloVera: A Multilingual Allophone Database0
Speaker Recognition in Bengali Language from Nonlinear Features0
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are0
Transformer based Grapheme-to-Phoneme ConversionCode1
Speaker Diarization with Lexical Information0
Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?0
Improved Speech Representations with Multi-Target Autoregressive Predictive Coding0
Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR0
Improving Readability for Automatic Speech Recognition Transcription0
An investigation of phone-based subword units for end-to-end speech recognition0
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech0
Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition0
Evaluating the Communication Efficiency in Federated Learning Algorithms0
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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