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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 2501–2550 of 3012 papers

TitleStatusHype
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding—0
Semantic Language Model for Tunisian Dialect—0
Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction—0
Semantic-WER: A Unified Metric for the Evaluation of ASR Transcript for End Usability—0
Semi-Autoregressive Streaming ASR With Label Context—0
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech—0
Semi-supervised acoustic model training for speech with code-switching—0
Semi-supervised ASR by End-to-end Self-training—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
Semi-supervised Learning with Sparse Autoencoders in Phone Classification—0
Semi-Supervised Speech Recognition via Graph-based Temporal Classification—0
Sentence Boundary Augmentation For Neural Machine Translation Robustness—0
Sentence segmentation of aphasic speech—0
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities—0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation—0
Sequence-level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models—0
Sequence-level self-learning with multiple hypotheses—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning—0
Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding—0
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition—0
Sequence-to-sequence models in peer-to-peer learning: A practical application—0
Sequence Transduction with Graph-based Supervision—0
Sequential Editing for Lifelong Training of Speech Recognition Models—0
Sequential End-to-End Intent and Slot Label Classification and Localization—0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution—0
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition—0
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
使用生成對抗網路於強健式自動語音辨識的應用(Exploiting Generative Adversarial Network for Robustness Automatic Speech Recognition)—0
Short-Term Word-Learning in a Dynamically Changing Environment—0
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition—0
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation—0
Silent Speech Interfaces for Speech Restoration: A Review—0
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
Simulating ASR errors for training SLU systems—0
Simulating realistic speech overlaps improves multi-talker ASR—0
SimulSpeech: End-to-End Simultaneous Speech to Text Translation—0
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models—0
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS—0
Singing voice conversion with non-parallel data—0
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training—0
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition—0
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition—0
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling—0
SmarTerp: A CAI System to Support Simultaneous Interpreters in Real-Time—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