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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 2701–2750 of 3012 papers

TitleStatusHype
Simulating ASR errors for training SLU systems—0
Design and Development of Speech Corpora for Air Traffic Control Training—0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition—0
Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data—0
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task—0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines—0
Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech—0
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard—0
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic—0
Towards an Automatic Assessment of Crowdsourced Data for NLU—0
Towards Processing of the Oral History Interviews and Related Printed Documents—0
ASR for Documenting Acutely Under-Resourced Indigenous Languages—0
Automatic Documentation of ICD Codes with Far-Field Speech Recognition—0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
End-to-End Multimodal Speech Recognition—0
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model—0
Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach—0
Neural Network Language Modeling with Letter-based Features and Importance Sampling—0
Language Recognition using Time Delay Deep Neural Network—0
ESPnet: End-to-End Speech Processing Toolkit—0
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only—0
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines—0
Machine Speech Chain with One-shot Speaker Adaptation—0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline—0
Multi-Modal Data Augmentation for End-to-End ASR—0
Light Gated Recurrent Units for Speech RecognitionCode0
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion—0
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition—0
On Modular Training of Neural Acoustics-to-Word Model for LVCSR—0
Evaluating Automatic Speech Recognition in Translation—0
Challenges in Speech Recognition and Translation of High-Value Low-Density Polysynthetic Languages—0
On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models—0
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages—0
Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation EvaluationCode0
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition—0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription—0
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts—0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition—0
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model—0
Audio Adversarial Examples: Targeted Attacks on Speech-to-TextCode0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
New Baseline in Automatic Speech Recognition for Northern S\'ami—0
Learning Robust Dialog Policies in Noisy Environments—0
Building competitive direct acoustics-to-word models for English conversational speech recognition—0
An analysis of incorporating an external language model into a sequence-to-sequence model—0
Exploiting Nontrivial Connectivity for Automatic Speech Recognition—0
Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition—0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks—0
Speech recognition for medical conversations—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