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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 2151–2200 of 3012 papers

TitleStatusHype
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks—0
Multimodal Speech Recognition with Unstructured Audio Masking—0
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video—0
Multi-mode Transformer Transducer with Stochastic Future Context—0
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition—0
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer—0
Multiple Representation Transfer from Large Language Models to End-to-End ASR Systems—0
Multiple topic identification in human/human conversations—0
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech—0
Multiresolution and Multimodal Speech Recognition with Transformers—0
Multi-resolution location-based training for multi-channel continuous speech separation—0
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR—0
Multi-Span Acoustic Modelling using Raw Waveform Signals—0
Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews—0
Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages—0
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition—0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
Multi-Stream End-to-End Speech Recognition—0
Multi-style Training for South African Call Centre Audio—0
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR—0
Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model—0
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech—0
Multi-task Language Modeling for Improving Speech Recognition of Rare Words—0
Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances—0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition—0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
Multi-task Learning with Cross Attention for Keyword Spotting—0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding—0
Multi-Task Self-Supervised Pre-Training for Music Classification—0
Multi-Temporal Lip-Audio Memory for Visual Speech Recognition—0
Multi-turn RNN-T for streaming recognition of multi-party speech—0
Multi-user VoiceFilter-Lite via Attentive Speaker Embedding—0
Multi-view Attention-based Speech Enhancement Model for Noise-robust Automatic Speech Recognition—0
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition—0
MuST-C: a Multilingual Speech Translation Corpus—0
Mutually-Constrained Monotonic Multihead Attention for Online ASR—0
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition—0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances—0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space—0
NeMo: a toolkit for building AI applications using Neural Modules—0
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
Neural Kalman Filtering for Speech Enhancement—0
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion—0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration—0
Neural Morphological Analysis: Encoding-Decoding Canonical Segments—0
Neural Network Architectures for Arabic Dialect Identification—0
Neural Network-Based Modeling of Phonetic Durations—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