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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 1851–1900 of 3012 papers

TitleStatusHype
Leveraging Pre-trained Language Model for Speech Sentiment Analysis—0
Improving RNN-T ASR Performance with Date-Time and Location Awareness—0
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS—0
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition—0
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition—0
Unsupervised Automatic Speech Recognition: A Review—0
Sequential End-to-End Intent and Slot Label Classification and Localization—0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios—0
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement—0
Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio—0
Semantic-WER: A Unified Metric for the Evaluation of ASR Transcript for End Usability—0
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition—0
Improving low-resource ASR performance with untranscribed out-of-domain data—0
Dual Script E2E framework for Multilingual and Code-Switching ASR—0
Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding—0
A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data—0
End-to-end ASR to jointly predict transcriptions and linguistic annotations—0
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation—0
Developing ASR for Indonesian-English Bilingual Language Teaching—0
End-to-End Automatic Speech Recognition: Its Impact on the Workflowin Documenting Yoloxóchitl Mixtec—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
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
Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Accent Recognition with Hybrid Phonetic Features—0
Spectral modification for recognition of children’s speech undermismatched conditions—0
Personalized Keyphrase Detection using Speaker and Environment Information—0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Head-synchronous Decoding for Transformer-based Streaming ASR—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Discriminative Self-training for Punctuation Prediction—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
On Sampling-Based Training Criteria for Neural Language Modeling—0
Scene-aware Far-field Automatic Speech Recognition—0
Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers—0
Accented Speech Recognition: A Survey—0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era—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