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 61016150 of 6433 papers

TitleStatusHype
A LDA-Based Topic Classification Approach From Highly Imperfect Automatic Transcriptions0
A Corpus and Phonetic Dictionary for Tunisian Arabic Speech Recognition0
SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling0
Morpho-Syntactic Study of Errors from Speech Recognition System0
Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish0
A Conventional Orthography for Tunisian Arabic0
Using a Serious Game to Collect a Child Learner Speech Corpus0
Basque Speecon-like and Basque SpeechDat MDB-600: speech databases for the development of ASR technology for Basque0
Mapping Diatopic and Diachronic Variation in Spoken Czech: The ORTOFON and DIALEKT Corpora0
Extensions of the Sign Language Recognition and Translation Corpus RWTH-PHOENIX-Weather0
The WaveSurfer Automatic Speech Recognition Plugin0
Comparison of Gender- and Speaker-adaptive Emotion Recognition0
Designing the Latvian Speech Recognition Corpus0
The ETAPE speech processing evaluation0
The Sweet-Home speech and multimodal corpus for home automation interaction0
Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems0
On the use of a fuzzy classifier to speed up the Sp\_ToBI labeling of the Glissando Spanish corpus0
GlobalPhone: Pronunciation Dictionaries in 20 Languages0
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks0
Revising the annotation of a Broadcast News corpus: a linguistic approach0
N-gram Counts and Language Models from the Common Crawl0
Rediscovering 15 Years of Discoveries in Language Resources and Evaluation: The LREC Anthology Analysis0
IBM's Belief Tracker: Results On Dialog State Tracking Challenge Datasets0
Click or Type: An Analysis of Wizard's Interaction for Future Wizard Interface Design0
Speech-Enabled Computer-Aided Translation: A Satisfaction Survey with Post-Editor Trainees0
A Quantitative Insight into the Impact of Translation on Readability0
Recipes for building voice search UIs for automotive0
An explicit statistical model of learning lexical segmentation using multiple cues0
Minimum Translation Modeling with Recurrent Neural Networks0
CHISPA on the GO: A mobile Chinese-Spanish translation service for travellers in trouble0
Designing Language Technology Applications: A Wizard of Oz Driven Prototyping Framework0
Augmenting Translation Models with Simulated Acoustic Confusions for Improved Spoken Language Translation0
Morfessor 2.0: Toolkit for statistical morphological segmentation0
On the Use of Speech Recognition Techniques to Identify Bird Species0
Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech RecognitionCode0
Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN0
Joint Incremental Disfluency Detection and Dependency Parsing0
Learning Human Pose Estimation Features with Convolutional NetworksCode0
A Subband-Based SVM Front-End for Robust ASR0
Speech Recognition Front End Without Information Loss0
Do Deep Nets Really Need to be Deep?Code0
Error Detection in Automatic Speech Recognition0
雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識 (Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments) [In Chinese]0
Noise in Speech-to-Text Voice: Analysis of Errors and Feasibility of Phonetic Similarity for Their Correction0
Unsupervised Learning of Invariant Representations in Hierarchical Architectures0
Vision-Guided Robot Hearing0
Textual Inference and Meaning Representation in Human Robot Interaction0
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition0
Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?0
分頻式調變頻譜分解於強健性語音辨識 (Sub-band modulation spectrum factorization in robust speech recognition) [In Chinese]0
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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