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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 2201–2225 of 3012 papers

TitleStatusHype
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages—0
Context-Dependent Acoustic Modeling without Explicit Phone Clustering—0
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model—0
Coupled Training of Sequence-to-Sequence Models for Accented Speech RecognitionCode0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
DiscreTalk: Text-to-Speech as a Machine Translation Problem—0
Automatic Estimation of Intelligibility Measure for Consonants in Speech—0
Incremental Learning for End-to-End Automatic Speech Recognition—0
CTC-synchronous Training for Monotonic Attention ModelCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
The Perceptimatic English Benchmark for Speech Perception Models—0
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions—0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech—0
CEASR: A Corpus for Evaluating Automatic Speech Recognition—0
The SAFE-T Corpus: A New Resource for Simulated Public Safety Communications—0
Large Corpus of Czech Parliament Plenary Hearings—0
Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews—0
ATC-ANNO: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation—0
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications—0
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition—0
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling—0
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text—0
Evaluating and Improving Child-Directed Automatic Speech Recognition—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