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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 2151–2200 of 3012 papers

TitleStatusHype
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset—0
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge—0
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification TasksCode0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech—0
Learning to Count Words in Fluent Speech enables Online Speech RecognitionCode1
Contextual RNN-T For Open Domain ASR—0
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR—0
Transfer Learning for British Sign Language Modelling—0
Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings—0
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty—0
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer—0
Sur l'utilisation de la reconnaissance automatique de la parole pour l'aide au diagnostic diff\'erentiel entre la maladie de Parkinson et l'AMS (On using automatic speech recognition for the differential diagnosis of Parkinson's Disease and MSA This article presents a study regarding the contribution of automatic speech processing in the differential diagnosis between Parkinson's disease and MSA (Multi-System Atrophies))—0
Introduction d'informations s\'emantiques dans un syst\`eme de reconnaissance de la parole (Despite spectacular advances in recent years, the Automatic Speech Recognition (ASR) systems still make mistakes, especially in noisy environments)—0
Analyse de l'effet de la r\'everb\'eration sur la reconnaissance automatique de la parole (Analyzing how reverberation affects Automatic Speech Recognition)—0
Reconnaissance automatique de la parole : g\'en\'eration des prononciations non natives pour l'enrichissement du lexique (In this study we propose a method for lexicon adaptation in order to improve the automatic speech recognition (ASR) of non-native speakers)—0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems—0
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features—0
Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition—0
Dynamic Masking for Improved Stability in Spoken Language Translation—0
On the Comparison of Popular End-to-End Models for Large Scale Speech RecognitionCode1
FT Speech: Danish Parliament Speech Corpus—0
Adapting End-to-End Speech Recognition for Readable SubtitlesCode1
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering—0
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
End-to-end Named Entity Recognition from English SpeechCode1
Large scale evaluation of importance maps in automatic speech recognition—0
PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASRCode1
Investigation of Large-Margin Softmax in Neural Language Modeling—0
Early Stage LM Integration Using Local and Global Log-Linear Combination—0
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition—0
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion—0
Iterative Pseudo-Labeling for Speech RecognitionCode0
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech RecognitionCode0
Improved Noisy Student Training for Automatic Speech RecognitionCode1
Enhancing Monotonic Multihead Attention for Streaming ASRCode1
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models—0
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech RecognitionCode1
An Effective End-to-End Modeling Approach for Mispronunciation Detection—0
Audio-visual Multi-channel Recognition of Overlapped Speech—0
Weak-Attention Suppression For Transformer Based Speech Recognition—0
Quaternion Neural Networks for Multi-channel Distant Speech Recognition—0
Dynamic Sparsity Neural Networks for Automatic Speech Recognition—0
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition—0
Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss—0
Conformer: Convolution-augmented Transformer for Speech RecognitionCode3
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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