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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 2676–2700 of 3012 papers

TitleStatusHype
LSTM Benchmarks for Deep Learning FrameworksCode0
Binarized LSTM Language Model—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
Role-specific Language Models for Processing Recorded Neuropsychological Exams—0
End-to-end named entity extraction from speech—0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio—0
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks—0
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2SeqCode0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
Targeted Adversarial Examples for Black Box Audio SystemsCode0
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces—0
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese—0
Improved ASR for Under-Resourced Languages Through Multi-Task Learning with Acoustic Landmarks—0
Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations—0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training—0
An Application for Building a Polish Telephone Speech Corpus—0
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation—0
Interpersonal Relationship Labels for the CALLHOME CorpusCode0
Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research—0
Open ASR for Icelandic: Resources and a Baseline System—0
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)—0
Phonetically Balanced Code-Mixed Speech Corpus for Hindi-English Automatic Speech Recognition—0
Pronunciation Variants and ASR of Colloquial Speech: A Case Study on Czech—0
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models—0
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach—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