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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 901–950 of 3012 papers

TitleStatusHype
Differentially Private Speaker Anonymization—0
Digits micro-model for accurate and secure transactions—0
Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings—0
Direct Acoustics-to-Word Models for English Conversational Speech Recognition—0
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech—0
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization—0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition—0
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments—0
Direct Speech to Speech Translation: A Review—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
DualVoice: Speech Interaction that Discriminates between Normal and Whispered Voice Input—0
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool—0
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach—0
Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions—0
DiscreTalk: Text-to-Speech as a Machine Translation Problem—0
Automatic Speech Recognition for Biomedical Data in Bengali Language—0
Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking—0
Discriminative Self-training for Punctuation Prediction—0
Discriminative Speech Recognition Rescoring with Pre-trained Language Models—0
Discriminative training of RNNLMs with the average word error criterion—0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation—0
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion—0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
Capitalization and Punctuation Restoration: a Survey—0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Can Whisper perform speech-based in-context learning?—0
Are disentangled representations all you need to build speaker anonymization systems?—0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model—0
DuDe: Dual-Decoder Multilingual ASR for Indian Languages using Common Label Set—0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition—0
Distributed representation and estimation of WFST-based n-gram models—0
Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition—0
DNCASR: End-to-End Training for Speaker-Attributed ASR—0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech—0
A Recorded Debating Dataset—0
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding—0
Automatic speech recognition in the diagnosis of primary progressive aphasia—0
An analysis of degenerating speech due to progressive dysarthria on ASR performance—0
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Does Whisper understand Swiss German? An automatic, qualitative, and human evaluation—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models—0
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems—0
Automatic Speech Recognition System-Independent Word Error Rate Estimation—0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?—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