https://github.com/anton-jeran/FAST-RIR
공식 코드FAST-RIR: Fast neural diffuse room impulse response generator
Papers with Code 아카이브
Speech Recognition
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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.
<span style="color:grey; opacity: 0.6">( Image credit: SpecAugment )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/anton-kashkin/hifi_vc
공식 코드https://github.com/antoniosudoso/attention-nilm
공식 코드Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/apple/dmel
공식 코드https://github.com/apple/ml-sigma-reparam
공식 코드Stabilizing Transformer Training by Preventing Attention Entropy Collapse
https://github.com/apple/ml-transcript-translation-consistency-ratings
공식 코드https://github.com/apptek/arabicdiacritizationinterspeech2023
공식 코드Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text
https://github.com/aquastar/csur_bridge_spectral_spatial_gnn_survey
공식 코드Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
https://github.com/archiki/Robust-E2E-ASR
공식 코드An Investigation of End-to-End Models for Robust Speech Recognition
https://github.com/areffarhadi/gammatonegram_cnn_dysarthric_speech
공식 코드https://github.com/areuther/ai-accelerators
공식 코드https://github.com/artbataev/uol_final
공식 코드RNN-Transducer-based Losses for Speech Recognition on Noisy Targets
https://github.com/articulatory/articulatory
공식 코드Deep Speech Synthesis from MRI-Based Articulatory Representations
https://github.com/asappresearch/sew
공식 코드Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
https://github.com/asappresearch/slue-toolkit
공식 코드SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
https://github.com/asappresearch/spoken-ner
공식 코드On the Use of External Data for Spoken Named Entity Recognition
https://github.com/asappresearch/wav2seq
공식 코드Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
https://github.com/aslp-lab/osum
공식 코드OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
https://github.com/astorfi/lip-reading-deeplearning
공식 코드3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
https://github.com/atharva7k/mms-code-switching
공식 코드Adapting the adapters for code-switching in multilingual ASR
https://github.com/athena-team/athena
공식 코드A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition
https://github.com/audioku/meta-transfer-learning
공식 코드https://github.com/audiollms/singlish
공식 코드Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models
https://github.com/audioshake/alt-eval
공식 코드https://github.com/augmented-human-lab/interspeech25_speechtherapy
공식 코드Towards Temporally Explainable Dysarthric Speech Clarity Assessment
https://github.com/augustgw/early-exit-transformer
공식 코드https://github.com/automl/NASLib
공식 코드https://github.com/aws-samples/seq2seq-asr-misbehaves
공식 코드Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances
https://github.com/awslabs/speech-representations
공식 코드https://github.com/ayushayt/imaginedspeechrecognition
공식 코드Analysis of EEG frequency bands for Envisioned Speech Recognition
https://github.com/b04901014/FG-transformer-TTS
공식 코드Fine-grained style control in Transformer-based Text-to-speech Synthesis
https://github.com/b04901014/FT-w2v2-ser
공식 코드Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition
https://github.com/b04901014/ISGAN
공식 코드Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech
https://github.com/b04901014/mqtts
공식 코드A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech
https://github.com/b08202033/whisper_prompting
공식 코드Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper
https://github.com/babe269/performant
공식 코드Visualising Model Training via Vowel Space for Text-To-Speech Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.