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 44514500 of 6433 papers

TitleStatusHype
Motivations, challenges, and perspectives for the development of an Automatic Speech Recognition System for the under-resourced Ngiemboon Language0
Garnishing a phonetic dictionary for ASR intake0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English0
Semantic Language Model for Tunisian Dialect0
Human-Informed Speakers and Interpreters Analysis in the WAW Corpus and an Automatic Method for Calculating Interpreters' D\'ecalage0
Towards Accurate Text Verbalization for ASR Based on Audio Alignment0
A Probabilistic Approach for Confidence Scoring in Speech Recognition0
The Ambiguous World of Emotion Representation0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
Estimation of a function of low local dimensionality by deep neural networks0
Two-Pass End-to-End Speech RecognitionCode0
MASR: A Modular Accelerator for Sparse RNNs0
Self-reinforcing Unsupervised Matching0
Deploying Technology to Save Endangered Languages0
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance0
AI and Accessibility: A Discussion of Ethical Considerations0
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews0
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning0
IMS-Speech: A Speech to Text Tool0
Personal VAD: Speaker-Conditioned Voice Activity DetectionCode0
Space-time error estimates for deep neural network approximations for differential equations0
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription0
Emotionless: Privacy-Preserving Speech Analysis for Voice AssistantsCode1
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling0
Challenging the Boundaries of Speech Recognition: The MALACH Corpus0
Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition0
Mitigating Noisy Inputs for Question Answering0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging0
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network0
Practical Speech Recognition with HTK0
Random Directional Attack for Fooling Deep Neural NetworksCode0
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems0
V2S attack: building DNN-based voice conversion from automatic speaker verification0
SANTLR: Speech Annotation Toolkit for Low Resource Languages0
DELTA: A DEep learning based Language Technology plAtformCode0
Multilingual Speech Recognition with Corpus Relatedness Sampling0
A Speech Test Set of Practice Business Presentations with Additional Relevant Texts0
Toward Automated Content Feedback Generation for Non-native Spontaneous Speech0
Learning Joint Acoustic-Phonetic Word Embeddings0
Speech Recognition for Tigrinya language Using Deep Neural Network Approach0
Personalizing ASR for Dysarthric and Accented Speech with Limited DataCode0
DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting0
MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the BibleCode0
Multi-Frame Cross-Entropy Training for Convolutional Neural Networks in Speech Recognition0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement0
A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data0
Cross-Attention End-to-End ASR for Two-Party Conversations0
2D-CTC for Scene Text Recognition0
Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research Survey0
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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