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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 851–900 of 3012 papers

TitleStatusHype
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition—0
Deep Recurrent Neural Networks for Acoustic Modelling—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Deep Shallow Fusion for RNN-T Personalization—0
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR—0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video—0
Deep Xi as a Front-End for Robust Automatic Speech Recognition—0
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Deliberation Model Based Two-Pass End-to-End Speech Recognition—0
Deliberation Model for On-Device Spoken Language Understanding—0
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems—0
DENOASR: Debiasing ASRs through Selective Denoising—0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
Densely Connected Convolutional Networks for Speech Recognition—0
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems—0
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition—0
Deploying Technology to Save Endangered Languages—0
Design and Development of Speech Corpora for Air Traffic Control Training—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty—0
Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts—0
Aligning Speech to Languages to Enhance Code-switching Speech Recognition—0
Developing ASR for Indonesian-English Bilingual Language Teaching—0
Developing Automatic Speech Recognition for Scottish Gaelic—0
Developing language technology tools and resources for a resource-poor language: Sindhi—0
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability—0
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori—0
D\'eveloppement de ressources en swahili pour un syt\`eme de reconnaisance automatique de la parole (Developments of Swahili resources for an automatic speech recognition system) [in French]—0
Device Directedness with Contextual Cues for Spoken Dialog Systems—0
Device-directed Utterance Detection—0
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition—0
Convolutional Speech Recognition with Pitch and Voice Quality Features—0
Diacritic Recognition Performance in Arabic ASR—0
Convoifilter: A case study of doing cocktail party speech recognition—0
Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021—0
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT—0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English—0
Conversational Speech Recognition Needs Data? Experiments with Austrian German—0
Dialogue Act Segmentation for Vietnamese Human-Human Conversational Texts—0
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition—0
Aligning Pre-trained Models for Spoken Language Translation—0
Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation—0
Conversational Speech Recognition By Learning Conversation-level Characteristics—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