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 31013150 of 6433 papers

TitleStatusHype
Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition0
W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-TrainingCode3
An empirical assessment of deep learning approaches to task-oriented dialog management0
Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach0
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent ClassificationCode1
Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features0
Dyn-ASR: Compact, Multilingual Speech Recognition via Spoken Language and Accent Identification0
Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity0
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation0
Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation0
Amortized Neural Networks for Low-Latency Speech Recognition0
Learning a Neural Diff for Speech Models0
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization0
User-Initiated Repetition-Based Recovery in Multi-Utterance Dialogue Systems0
Automatic recognition of suprasegmentals in speech0
Decoupling recognition and transcription in Mandarin ASR0
The Role of Phonetic Units in Speech Emotion Recognition0
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks0
Adversarial Data Augmentation for Disordered Speech Recognition0
Interactive Reinforcement Learning for Table Balancing Robot0
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 20210
KIT’s IWSLT 2021 Offline Speech Translation System0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task0
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 20210
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks0
ZJU’s IWSLT 2021 Speech Translation System0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions0
How Might We Create Better Benchmarks for Speech Recognition?0
Avengers, Ensemble! Benefits of ensembling in grapheme-to-phoneme prediction0
A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility0
Automatic generation of a 3D sign language avatar on AR glasses given 2D videos of human signers0
Technology-Augmented Multilingual Communication Models: New Interaction Paradigms, Shifts in the Language Services Industry, and Implications for Training Programs0
基于改进Conformer的新闻领域端到端语音识别(End-to-End Speech Recognition in News Field based on Conformer)0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text0
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus0
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental0
The History of Speech Recognition to the Year 2030Code1
USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition ExperimentsCode1
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers0
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning0
SVEva Fair: A Framework for Evaluating Fairness in Speaker VerificationCode1
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations0
Differentiable Allophone Graphs for Language-Universal Speech RecognitionCode0
Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition0
OLR 2021 Challenge: Datasets, Rules and Baselines0
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0Code1
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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