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 49014950 of 6433 papers

TitleStatusHype
Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper0
會議語音辨識使用語者資訊之語言模型調適技術 (On the Use of Speaker-Aware Language Model Adaptation Techniques for Meeting Speech Recognition ) [In Chinese]0
Neural Speech Translation at AppTek0
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models0
A Morphological Analyzer for Shipibo-Konibo0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding0
探討聲學模型的合併技術與半監督鑑別式訓練於會議語音辨識之研究 (Investigating acoustic model combination and semi-supervised discriminative training for meeting speech recognition) [In Chinese]0
Improving Neural Language Models with Weight Norm Initialization and Regularization0
Using Spoken Word Posterior Features in Neural Machine Translation0
Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task0
Joint On-line Learning of a Zero-shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction0
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models0
Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture0
Characterizing Audio Adversarial Examples Using Temporal Dependency0
End-to-End Multi-Lingual Multi-Speaker Speech Recognition0
Zero-shot Learning for Speech Recognition with Universal Phonetic Model0
EXPLORATION OF EFFICIENT ON-DEVICE ACOUSTIC MODELING WITH NEURAL NETWORKS0
Non-native children speech recognition through transfer learning0
From Audio to Semantics: Approaches to end-to-end spoken language understanding0
Low Frequency Adversarial PerturbationCode0
Hindi-English Code-Switching Speech Corpus0
Perfect match: Improved cross-modal embeddings for audio-visual synchronisation0
Scene Text Recognition from Two-Dimensional Perspective0
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network0
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm0
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models0
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition0
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations0
End-to-end speech recognition using lattice-free MMI0
Pre-training on high-resource speech recognition improves low-resource speech-to-text translationCode0
Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique0
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition0
LRS3-TED: a large-scale dataset for visual speech recognitionCode0
Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural NetworksCode0
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial NetworksCode0
AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale0
End-to-end Speech Recognition with Adaptive Computation Steps0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Mean Field Analysis of Neural Networks: A Central Limit Theorem0
Augmenting Bottleneck Features of Deep Neural Network Employing Motor State for Speech Recognition at Humanoid Robots0
WiSeBE: Window-based Sentence Boundary EvaluationCode0
Large Margin Neural Language Model0
Role of Intonation in Scoring Spoken English0
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks0
Linked Recurrent Neural Networks0
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing0
Computing Word Classes Using Spectral Clustering0
Neural Architecture Search: A SurveyCode0
Toward domain-invariant speech recognition via large scale training0
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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