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 51515200 of 6433 papers

TitleStatusHype
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens0
Zero-shot Learning for Speech Recognition with Universal Phonetic Model0
Zero-Shot Learning of Language Models for Describing Human Actions Based on Semantic Compositionality of Actions0
Zero-shot Speech Translation0
Zero Shot Text to Speech Augmentation for Automatic Speech Recognition on Low-Resource Accented Speech Corpora0
Zipformer: A faster and better encoder for automatic speech recognition0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities0
0/1 Deep Neural Networks via Block Coordinate Descent0
ZJU’s IWSLT 2021 Speech Translation System0
100,000 Podcasts: A Spoken English Document Corpus0
11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks0
Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry0
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition0
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method0
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech0
An Efficient Pre-processing Method to Eliminate Adversarial Effects0
Fluent Translations from Disfluent Speech in End-to-End Speech Translation0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain0
Improving Minimal Gated Unit for Sequential Data0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains0
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition0
Persian Signature Verification using Fully Convolutional Networks0
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR0
Transformer-based Cascaded Multimodal Speech Translation0
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation0
1SPU: 1-step Speech Processing Unit0
2020福爾摩沙臺語語音辨識比賽之初步實驗 (A Preliminary Study of Formosa Speech Recognition Challenge 2020 – Taiwanese ASR)0
Improving noisy student training for low-resource languages in End-to-End ASR using CycleGAN and inter-domain losses0
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks0
Towards interfacing large language models with ASR systems using confidence measures and prompting0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition0
Handling Numeric Expressions in Automatic Speech Recognition0
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation0
SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data0
Self-Supervised Learning for Multi-Channel Neural Transducer0
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder0
Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio0
LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors0
2-bit Conformer quantization for automatic speech recognition0
2D-CTC for Scene Text Recognition0
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition0
3D Feature Pyramid Attention Module for Robust Visual Speech Recognition0
4-bit Quantization of LSTM-based Speech Recognition Models0
Joint Beam Search Integrating CTC, Attention, and Transducer Decoders0
4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders0
使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]0
使用字典學習法於強健性語音辨識(The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]0
使用長短期記憶類神經網路建構中文語音辨識器之研究 (A study on Mandarin speech recognition using Long Short-Term Memory neural network) [In Chinese]0
A 71.2-μW Speech Recognition Accelerator with Recurrent Spiking Neural Network0
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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