SOTAVerified

Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

Showing 41514200 of 6433 papers

TitleStatusHype
Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition0
Macsen: A Voice Assistant for Speakers of a Lesser Resourced LanguageCode0
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English0
Crossing the SSH Bridge with Interview Data0
Malayalam Speech Corpus: Design and Development for Dravidian Language0
ATC-ANNO: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary0
The SAFE-T Corpus: A New Resource for Simulated Public Safety Communications0
Parallel Corpus for Japanese Spoken-to-Written Style Conversion0
Gender Detection from Human Voice Using Tensor Analysis0
Samr\'omur: Crowd-sourcing Data Collection for Icelandic Speech Recognition0
Multi-head Monotonic Chunkwise Attention For Online Speech Recognition0
Automatically Assess Children's Reading Skills0
Fully Convolutional ASR for Less-Resourced Endangered Languages0
Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition0
Improving the Language Model for Low-Resource ASR with Online Text Corpora0
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician0
Large Corpus of Czech Parliament Plenary Hearings0
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval0
Evaluating and Improving Child-Directed Automatic Speech Recognition0
CoBiLiRo: A Research Platform for Bimodal Corpora0
LinTO Platform: A Smart Open Voice Assistant for Business Environments0
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition0
Development and Evaluation of Speech Synthesis Corpora for Latvian0
Style Variation as a Vantage Point for Code-Switching0
Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU0
Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages0
CEASR: A Corpus for Evaluating Automatic Speech Recognition0
Using Automatic Speech Recognition in Spoken Corpus Curation0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition0
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?0
Challenges of Applying Automatic Speech Recognition for Transcribing EU Parliament Committee Meetings: A Pilot Study0
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information0
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech0
The 2019 BBN Cross-lingual Information Retrieval System0
Class-based LSTM Russian Language Model with Linguistic Information0
Analysis of GlobalPhone and Ethiopian Languages Speech Corpora for Multilingual ASR0
Speech Transcription Challenges for Resource Constrained Indigenous Language Cree0
Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews0
A CLARIN Transcription Portal for Interview Data0
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling0
Towards an Efficient Code-Mixed Grapheme-to-Phoneme Conversion in an Agglutinative Language: A Case Study on To-Korean Transliteration0
Learning to Rank Intents in Voice Assistants0
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTubeCode0
Multiresolution and Multimodal Speech Recognition with Transformers0
Neural Speech Separation Using Spatially Distributed Microphones0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified