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Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.

Papers

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Showing 2901–2950 of 3012 papers

TitleStatusHype
Developing language technology tools and resources for a resource-poor language: Sindhi—0
An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition—0
An Attentional Model for Speech Translation Without Transcription—0
Using Related Languages to Enhance Statistical Language Models—0
A Joint Model of Orthography and Morphological Segmentation—0
Phonological Pun-derstanding—0
Lecture Translator - Speech translation framework for simultaneous lecture translation—0
The IBM Speaker Recognition System: Recent Advances and Error Analysis—0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation—0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans—0
Towards Automatic Transcription of ILSE ― an Interdisciplinary Longitudinal Study of Adult Development and Aging—0
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource—0
The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus—0
Enhanced CORILGA: Introducing the Automatic Phonetic Alignment Tool for Continuous Speech—0
Generating Task-Pertinent sorted Error Lists for Speech Recognition—0
The DIRHA Portuguese Corpus: A Comparison of Home Automation Command Detection and Recognition in Simulated and Real Data.—0
Operational Assessment of Keyword Search on Oral History—0
Endangered Language Documentation: Bootstrapping a Chatino Speech Corpus, Forced Aligner, ASR—0
A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems—0
Joining-in-type Humanoid Robot Assisted Language Learning System—0
Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation—0
Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of WolofCode0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA ProjectCode0
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection—0
The IBM 2016 Speaker Recognition System—0
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation—0
Automatic recognition of element classes and boundaries in the birdsong with variable sequences—0
Sparse Non-negative Matrix Language Modeling—0
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media—0
Spoken Language Translation for Polish—0
Blending LSTMs into CNNs—0
Enhancements in statistical spoken language translation by de-normalization of ASR results—0
Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation—0
Towards Structured Deep Neural Network for Automatic Speech Recognition—0
類神經網路訓練結合環境群集及專家混合系統於強健性語音辨識(Automatic Speech Recognition using Neural Network based Acoustic Model with the Environment Clustering and Mixture of Experts Algorithms) [In Chinese]—0
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge—0
The USFD Spoken Language Translation System for IWSLT 2014—0
Data-selective Transfer Learning for Multi-Domain Speech Recognition—0
Unsupervised Domain Discovery using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition—0
Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence—0
EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based DecodingCode0
Neural NILM: Deep Neural Networks Applied to Energy DisaggregationCode0
Incremental LSTM-based Dialog State Tracker—0
Driving ROVER with Segment-based ASR Quality Estimation—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
Recognize Foreign Low-Frequency Words with Similar Pairs—0
Hybridized Feature Extraction and Acoustic Modelling Approach for Dysarthric Speech Recognition—0
Towards Structured Deep Neural Network for Automatic Speech Recognition—0
Recurrent Neural Network Training with Dark Knowledge Transfer—0
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1—Unverified
2TM-seq2seqTest WER9.7—Unverified
3CTC/attentionTest WER8.2—Unverified
4LF-MMI TDNNTest WER6.7—Unverified
5Whisper-LLaMATest WER6.6—Unverified
6End2end ConformerTest WER3.9—Unverified
7End2end ConformerTest WER3.7—Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7—Unverified
9CTC/AttentionTest WER1.5—Unverified
10WhisperTest WER1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5—Unverified
2CleanMel-L-maskCER14.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32—Unverified
2Whisper-largev3-finetunedTest WER10.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89—Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified