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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 1476–1500 of 3012 papers

TitleStatusHype
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition—0
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems—0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
End-to-end model for named entity recognition from speech without paired training data—0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding—0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition—0
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR—0
End-to-End Multi-speaker ASR with Independent Vector Analysis—0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation—0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge—0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition—0
Effectiveness of text to speech pseudo labels for forced alignment and cross lingual pretrained models for low resource speech recognition—0
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings—0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition—0
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data—0
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Memory-Efficient Training of RNN-Transducer with Sampled Softmax—0
Is Word Error Rate a good evaluation metric for Speech Recognition in Indic Languages?—0
Improving Speech Recognition for Indic Languages using Language Model—0
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech RecognitionCode0
Code Switched and Code Mixed Speech Recognition for Indic languages—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition With Emformer—0
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
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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