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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 1901–1950 of 3012 papers

TitleStatusHype
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme—0
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data—0
Scalable Multi Corpora Neural Language Models for ASR—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Scaling ASR Improves Zero and Few Shot Learning—0
Scaling Up Deliberation for Multilingual ASR—0
Scene-aware Far-field Automatic Speech Recognition—0
SC-MoE: Switch Conformer Mixture of Experts for Unified Streaming and Non-streaming Code-Switching ASR—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
SE-Bridge: Speech Enhancement with Consistent Brownian Bridge—0
Security and Privacy Problems in Voice Assistant Applications: A Survey—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models—0
Segment-Level Vectorized Beam Search Based on Partially Autoregressive Inference—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—0
Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Self-Normalized Importance Sampling for Neural Language Modeling—0
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition—0
Self-Supervised Learning-Based Source Separation for Meeting Data—0
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations—0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Self-supervised representations in speech-based depression detection—0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text—0
Self-Supervised Speech Representation Learning: A Review—0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
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
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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