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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 1901–1950 of 3012 papers

TitleStatusHype
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
Word-level confidence estimation for RNN transducers—0
Word Order Does Not Matter For Speech Recognition—0
Words Worth: Verbal Content and Hirability Impressions in YouTube Video Resumes—0
Word Transduction for Addressing the OOV Problem in Machine Translation for Similar Resource-Scarce Languages—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish—0
Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication—0
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus—0
Zero-resource Speech Translation and Recognition with LLMs—0
Zero-Shot Automatic Pronunciation Assessment—0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition—0
Zero-shot Disfluency Detection for Indian Languages—0
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens—0
Zero-shot Speech Translation—0
Zero Shot Text to Speech Augmentation for Automatic Speech Recognition on Low-Resource Accented Speech Corpora—0
Zipformer: A faster and better encoder for automatic speech recognition—0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities—0
100,000 Podcasts: A Spoken English Document Corpus—0
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition—0
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition—0
Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers—0
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks—0
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers—0
LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration—0
Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition—0
Language Agnostic Data-Driven Inverse Text Normalization—0
Language Bias in Self-Supervised Learning For Automatic Speech Recognition—0
Language Dependencies in Adversarial Attacks on Speech Recognition Systems—0
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data—0
Language Modeling with Highway LSTM—0
Language Recognition using Time Delay Deep Neural Network—0
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes—0
Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR—0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
Large Corpus of Czech Parliament Plenary Hearings—0
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition—0
Large Language Models based ASR Error Correction for Child Conversations—0
Large scale evaluation of importance maps in automatic speech recognition—0
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition—0
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model—0
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta—0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo, and Wolaytta—0
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning—0
Late fusion ensembles for speech recognition on diverse input audio representations—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation—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