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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 2851–2900 of 3012 papers

TitleStatusHype
The IWSLT 2016 Evaluation Campaign—0
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words—0
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums—0
Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—0
Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts—0
A non-expert Kaldi recipe for Vietnamese Speech Recognition System—0
Automated speech-unit delimitation in spoken learner English—0
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network—0
An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation—0
Using Ambiguity Detection to Streamline Linguistic Annotation—0
RACAI Entry for the IWSLT 2016 Shared Task—0
Invariant Representations for Noisy Speech Recognition—0
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition—0
Audio Visual Speech Recognition using Deep Recurrent Neural Networks—0
Automatic recognition of child speech for robotic applications in noisy environments—0
Latent Tree Language ModelCode0
Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts—0
Codeswitching Detection via Lexical Features in Conditional Random Fields—0
Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling—0
Neural Morphological Analysis: Encoding-Decoding Canonical Segments—0
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding—0
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech—0
Challenges of Computational Processing of Code-Switching—0
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models—0
Semi-supervised Learning with Sparse Autoencoders in Phone Classification—0
使用字典學習法於強健性語音辨識(The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]—0
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition—0
Minimally Supervised Written-to-Spoken Text Normalization—0
An Adaptive Psychoacoustic Model for Automatic Speech Recognition—0
Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms—0
On the verbalization patterns of part-whole relations in isiZulu—0
How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents—0
Off-topic Response Detection for Spontaneous Spoken English Assessment—0
Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts—0
Modeling Concept Dependencies in a Scientific Corpus—0
TranscRater: a Tool for Automatic Speech Recognition Quality Estimation—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—0
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Pynini: A Python library for weighted finite-state grammar compilation—0
Unsupervised morph segmentation and statistical language models for vocabulary expansion—0
Distributed representation and estimation of WFST-based n-gram models—0
From Human Language Technology to Human Language Science—0
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition—0
A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition—0
Graph based manifold regularized deep neural networks for automatic speech recognition—0
Training variance and performance evaluation of neural networks in speech—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
Automated scoring across different modalities—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