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 51015150 of 6433 papers

TitleStatusHype
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models0
FARMI: A FrAmework for Recording Multi-Modal Interactions0
Evaluation Phonemic Transcription of Low-Resource Tonal Languages for Language DocumentationCode0
VAST: A Corpus of Video Annotation for Speech Technologies0
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings0
From `Solved Problems' to New Challenges: A Report on LDC Activities0
Text Normalization Infrastructure that Scales to Hundreds of Language Varieties0
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic0
A Leveled Reading Corpus of Modern Standard Arabic0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition0
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)0
Towards an Automatic Assessment of Crowdsourced Data for NLU0
Using Discourse Information for Education with a Spanish-Chinese Parallel Corpus0
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas0
A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages0
MirasVoice: A bilingual (English-Persian) speech corpus0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines0
DART: A Large Dataset of Dialectal Arabic Tweets0
Increasing the Accessibility of Time-Aligned Speech Corpora with Spokes Mix0
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation0
Neural Caption Generation for News Images0
Pronunciation Variants and ASR of Colloquial Speech: A Case Study on Czech0
TF-LM: TensorFlow-based Language Modeling ToolkitCode0
Preparing Data from Psychotherapy for Natural Language ProcessingCode0
Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus0
Collecting Code-Switched Data from Social Media0
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task0
Design and Development of Speech Corpora for Air Traffic Control Training0
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition0
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach0
An Application for Building a Polish Telephone Speech Corpus0
Simulating ASR errors for training SLU systems0
Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech0
Investigations on End-to-End Audiovisual Fusion0
Automatic Documentation of ICD Codes with Far-Field Speech Recognition0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip0
Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning ClustersCode0
End-to-End Multimodal Speech Recognition0
Recent Progresses in Deep Learning based Acoustic Models (Updated)0
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model0
An Information-Theoretic View for Deep Learning0
Multi-Head Decoder for End-to-End Speech Recognition0
Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach0
Neural Compatibility Modeling with Attentive Knowledge Distillation0
Twin Regularization for online speech recognitionCode0
Neural Network Language Modeling with Letter-based Features and Importance Sampling0
Language Recognition using Time Delay Deep Neural Network0
Global SNR Estimation of Speech Signals using Entropy and Uncertainty Estimates from Dropout Networks0
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of Untranscribed Speech0
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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