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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 1301–1350 of 3012 papers

TitleStatusHype
Improved far-field speech recognition using Joint Variational Autoencoder—0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment—0
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation—0
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition—0
Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking—0
Automated speech tools for helping communities process restricted-access corpora for language revival efforts—0
Lombard Effect for Bilingual Speakers in Cantonese and English: importance of spectro-temporal features—0
Study of Indian English Pronunciation Variabilities relative to Received Pronunciation—0
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes—0
Self-critical Sequence Training for Automatic Speech Recognition—0
HuBERT-EE: Early Exiting HuBERT for Efficient Speech RecognitionCode0
ASR in German: A Detailed Error Analysis—0
Large-Scale Streaming End-to-End Speech Translation with Neural TransducersCode1
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data—0
Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility PredictionCode0
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition—0
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition—0
Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation—0
Unsupervised Data Selection via Discrete Speech Representation for ASR—0
Audio-visual multi-channel speech separation, dereverberation and recognition—0
Towards End-to-end Unsupervised Speech Recognition—0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
Deliberation Model for On-Device Spoken Language Understanding—0
An Analysis of Semantically-Aligned Speech-Text Embeddings—0
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
PriMock57: A Dataset Of Primary Care Mock ConsultationsCode1
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR—0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition—0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding—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
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives—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
Memory-Efficient Training of RNN-Transducer with Sampled Softmax—0
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker ExtractionCode1
indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languagesCode1
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data—0
How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control CommunicationsCode1
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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