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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 2751–2800 of 3012 papers

TitleStatusHype
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition—0
Phonemic and Graphemic Multilingual CTC Based Speech Recognition—0
Multilingual Speech Recognition With A Single End-To-End Model—0
Dual Language Models for Code Switched Speech Recognition—0
Unsupervised Method for Improving Arabic Speech Recognition Systems—0
Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks—0
Improving Black-box Speech Recognition using Semantic Parsing—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning—0
A Study of All-Convolutional Encoders for Connectionist Temporal Classification—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
Syntactic and Semantic Features For Code-Switching Factored Language Models—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential DataCode0
Attention-based Wav2Text with Feature Transfer Learning—0
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition—0
Language Modeling with Highway LSTM—0
A Recorded Debating Dataset—0
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition SystemsCode0
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks—0
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the ArtCode0
Towards Quantum Language Models—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
Word Transduction for Addressing the OOV Problem in Machine Translation for Similar Resource-Scarce Languages—0
Amharic-English Speech Translation in Tourism Domain—0
A Text Normalisation System for Non-Standard English Words—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video—0
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks—0
Improving Machine Translation Quality Estimation with Neural Network Features—0
Enriching ASR Lattices with POS Tags for Dependency Parsing—0
Information Theoretic Analysis of DNN-HMM Acoustic Modeling—0
An Improved Residual LSTM Architecture for Acoustic Modeling—0
Dialogue Act Segmentation for Vietnamese Human-Human Conversational Texts—0
Language Identification Using Deep Convolutional Recurrent Neural NetworksCode0
Massively Multilingual Neural Grapheme-to-Phoneme ConversionCode0
Attentive listening system with backchanneling, response generation and flexible turn-taking—0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition—0
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training—0
Fast and Accurate OOV Decoder on High-Level Features—0
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation—0
Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking—0
Listening while Speaking: Speech Chain by Deep Learning—0
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies—0
Predicting Causes of Reformulation in Intelligent Assistants—0
Unsupervised Submodular Rank Aggregation on Score-based PermutationsCode0
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
Joint CTC/attention decoding for end-to-end speech recognition—0
Automatic Quality Estimation for ASR System Combination—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