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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 1776–1800 of 3012 papers

TitleStatusHype
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling InsightsCode1
Lightweight Adapter Tuning for Multilingual Speech TranslationCode1
Dual Script E2E framework for Multilingual and Code-Switching ASR—0
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition—0
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Improving low-resource ASR performance with untranscribed out-of-domain data—0
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation—0
End-to-End Automatic Speech Recognition: Its Impact on the Workflowin Documenting Yoloxóchitl Mixtec—0
Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding—0
Developing ASR for Indonesian-English Bilingual Language Teaching—0
End-to-end ASR to jointly predict transcriptions and linguistic annotations—0
A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data—0
Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR—0
Training Speech Enhancement Systems with Noisy Speech Datasets—0
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries—0
LiSTra, Automatic Speech Translation: English to Lingala case study—0
Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation—0
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End—0
Exploring CTC Based End-to-End Techniques for Myanmar Speech Recognition—0
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders—0
StutterNet: Stuttering Detection Using Time Delay Neural Network—0
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech TranslationCode1
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech—0
English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System—0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition—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