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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 501–550 of 3012 papers

TitleStatusHype
DENOASR: Debiasing ASRs through Selective Denoising—0
Improving Automatic Speech Recognition with Decoder-Centric Regularisation in Encoder-Decoder Models—0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation—0
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding—0
End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach—0
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup—0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation—0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Roadmap towards Superhuman Speech Understanding using Large Language Models—0
Automatic Speech Recognition with BERT and CTC Transformers: A Review—0
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities—0
A two-stage transliteration approach to improve performance of a multilingual ASR—0
Advocating Character Error Rate for Multilingual ASR Evaluation—0
CR-CTC: Consistency regularization on CTC for improved speech recognition—0
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges—0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—0
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition—0
Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems—0
Spoken Grammar Assessment Using LLM—0
Automatic Speech Recognition for the Ika Language—0
End-to-End Speech Recognition with Pre-trained Masked Language Model—0
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems—0
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility—0
Efficient Long-Form Speech Recognition for General Speech In-Context Learning—0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking—0
A GEN AI Framework for Medical Note Generation—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices—0
Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs—0
Revisiting Acoustic Features for Robust ASR—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error CorrectionCode0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder—0
A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection—0
Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech RecognitionCode0
Personalized Speech Recognition for Children with Test-Time Adaptation—0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
ASR Benchmarking: Need for a More Representative Conversational DatasetCode0
Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text—0
WER We Stand: Benchmarking Urdu ASR Models—0
M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses—0
Chain-of-Thought Prompting for Speech Translation—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