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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 1701–1750 of 3012 papers

TitleStatusHype
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models—0
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization—0
Operational Assessment of Keyword Search on Oral History—0
Opportunities & Challenges In Automatic Speech Recognition—0
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation—0
Optimizing Byte-level Representation for End-to-end ASR—0
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition—0
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning—0
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts—0
Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership—0
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification—0
MAC: A unified framework boosting low resource automatic speech recognition—0
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)—0
Parallel Corpus for Japanese Spoken-to-Written Style Conversion—0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions—0
ParlaSpeech-HR - a Freely Available ASR Dataset for Croatian Bootstrapped from the ParlaMint Corpus—0
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition—0
PATCorrect: Non-autoregressive Phoneme-augmented Transformer for ASR Error Correction—0
PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription—0
Perception of Phonological Assimilation by Neural Speech Recognition Models—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks—0
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect—0
Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition—0
Performance Monitoring for End-to-End Speech Recognition—0
Performant ASR Models for Medical Entities in Accented Speech—0
The Recognition Of Persian Phonemes Using PPNet—0
persoDA: Personalized Data Augmentation for Personalized ASR—0
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition—0
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets—0
Personalized Keyphrase Detection using Speaker and Environment Information—0
Personalized Predictive ASR for Latency Reduction in Voice Assistants—0
Personalized Query Rewriting in Conversational AI Agents—0
Personalized Speech Recognition for Children with Test-Time Adaptation—0
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation—0
PhaseFool: Phase-oriented Audio Adversarial Examples via Energy Dissipation—0
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models—0
Phoneme Recognition with Large Hierarchical Reservoirs—0
Phone Merging For Code-Switched Speech Recognition—0
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario—0
Phonemic and Graphemic Multilingual CTC Based Speech Recognition—0
Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili—0
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?—0
Phonetically Balanced Code-Mixed Speech Corpus for Hindi-English Automatic Speech Recognition—0
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription—0
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system—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