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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 1101–1150 of 3012 papers

TitleStatusHype
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding—0
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR—0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch—0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems—0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—0
ASR error management for improving spoken language understanding—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
Acoustic Model Fusion for End-to-end Speech Recognition—0
Accent Recognition with Hybrid Phonetic Features—0
Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties—0
ASR Error Detection via Audio-Transcript entailment—0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model—0
ASR Error Correction using Large Language Models—0
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task—0
Comparative Analysis of the wav2vec 2.0 Feature Extractor—0
ASR Error Correction and Domain Adaptation Using Machine Translation—0
A Generative Model of a Pronunciation Lexicon for Hindi—0
Acoustic Model Compression with MAP adaptation—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction—0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice—0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition—0
ASR Bundestag: A Large-Scale political debate dataset in German—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation—0
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums—0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
A GEN AI Framework for Medical Note Generation—0
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition—0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents—0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning—0
Code-Switching Text Generation and Injection in Mandarin-English ASR—0
Code-Switching Detection with Data-Augmented Acoustic and Language Models—0
ASR-Aware End-to-end Neural Diarization—0
AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition—0
Codeswitching Detection via Lexical Features in Conditional Random Fields—0
Code-Switching Detection Using ASR-Generated Language Posteriors—0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition—0
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences—0
Code Switched and Code Mixed Speech Recognition for Indic languages—0
Automatic Speech Recognition Advancements for Indigenous Languages of the Americas—0
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond—0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—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