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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 301–350 of 3012 papers

TitleStatusHype
A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC VideosCode0
A low latency attention module for streaming self-supervised speech representation learningCode0
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with AphasiaCode0
LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic ContextCode0
LSTM Benchmarks for Deep Learning FrameworksCode0
A Dataset for Speech Emotion Recognition in Greek Theatrical PlaysCode0
Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech RecognitionCode0
A Comparative Study on Transformer vs RNN in Speech ApplicationsCode0
An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer LearningCode0
Light Gated Recurrent Units for Speech RecognitionCode0
Linear Time Complexity Conformers with SummaryMixing for Streaming Speech RecognitionCode0
Measuring the Accuracy of Automatic Speech Recognition SolutionsCode0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Latent Tree Language ModelCode0
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task LearningCode0
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related TasksCode0
Leveraging Broadcast Media Subtitle Transcripts for Automatic Speech Recognition and SubtitlingCode0
Analyzing the impact of speaker localization errors on speech separation for automatic speech recognitionCode0
Adapting the adapters for code-switching in multilingual ASRCode0
Analyzing Robustness of End-to-End Neural Models for Automatic Speech RecognitionCode0
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech RecognitionCode0
Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech RecognitionCode0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition SystemsCode0
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative TrainingCode0
Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn'tCode0
Language Identification Using Deep Convolutional Recurrent Neural NetworksCode0
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distributionCode0
Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic InformationCode0
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptationCode0
Investigating the Effects of Word Substitution Errors on Sentence EmbeddingsCode0
Iterative Pseudo-Labeling for Speech RecognitionCode0
Kurdish (Sorani) Speech to Text: Presenting an Experimental DatasetCode0
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for ConversationsCode0
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech RecognitionCode0
Interpersonal Relationship Labels for the CALLHOME CorpusCode0
Improving Voice Separation by Incorporating End-to-end Speech RecognitionCode0
A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognitionCode0
A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision QuantizationCode0
Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN CorpusCode0
Intrinsic evaluation of language models for code-switchingCode0
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
Improving CTC-based speech recognition via knowledge transferring from pre-trained language modelsCode0
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech RecognitionCode0
AdaCS: Adaptive Normalization for Enhanced Code-Switching ASRCode0
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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