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 22512300 of 6433 papers

TitleStatusHype
Building a Functional Machine Translation Corpus for Kpelle0
An Overview of Hindi Speech Recognition0
Accelerating Transducers through Adjacent Token Merging0
Explaining Deep Neural Networks0
Explanations for Automatic Speech Recognition0
Building Accurate Low Latency ASR for Streaming Voice Search0
An Overview of BPPT's Indonesian Language Resources0
Advances in Very Deep Convolutional Neural Networks for LVCSR0
Error Correction in ASR using Sequence-to-Sequence Models0
Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition0
BUCEADOR, a multi-language search engine for digital libraries0
Error Analysis and Improving Speech Recognition for Latvian Language0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text0
A Novel Topology for End-to-end Temporal Classification and Segmentation with Recurrent Neural Network0
Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio0
Error Detection in Automatic Speech Recognition0
Building a 70 billion word corpus of English from ClueWeb0
ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations0
Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group0
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA0
ESPnet: End-to-End Speech Processing Toolkit0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration0
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding0
ESPnet-ST: All-in-One Speech Translation Toolkit0
ESPnet-ST IWSLT 2021 Offline Speech Translation System0
Building and Evaluation of a Real Room Impulse Response Dataset0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept0
Essence Knowledge Distillation for Speech Recognition0
ESSumm: Extractive Speech Summarization from Untranscribed Meeting0
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models0
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks0
Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance0
Estimating User Interest from Open-Domain Dialogue0
Estimation de la qualit\'e d'un syst\`eme de reconnaissance de la parole pour une t\^ache de compr\'ehension (Quality estimation of a Speech Recognition System for a Spoken Language Understanding task)0
Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space0
Etude de la performance des modèles acoustiques pour des voix de personnes âgées en vue de l'adaptation des systèmes de RAP (Assessment of the acoustic models performance in the ageing voice case for ASR system adaptation) [in French]0
Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique0
EURO: ESPnet Unsupervised ASR Open-source Toolkit0
Euronews: a multilingual speech corpus for ASR0
Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization0
Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts0
Evaluating and Improving Child-Directed Automatic Speech Recognition0
Evaluating and reducing the distance between synthetic and real speech distributions0
Evaluating Appropriateness Of System Responses In A Spoken CALL Game0
Evaluating a Spoken Dialogue System that Detects and Adapts to User Affective States0
Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfaces0
Evaluating Automatic Speech Recognition Systems in Comparison With Human Perception Results Using Distinctive Feature Measures0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset0
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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