Speech and Language Technology (SaLT) at the University of Stuttgart (@DigitalPhonetics)
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DigitalPhonetics

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  • Stars
    star
    624
  • Global Org. Rank 20,856 (Top 7 %)
  • Registered over 6 years ago
  • Most used languages
    Python
         85.7 %
    Shell
         7.1 %
    Go
         7.1 %
  • Location πŸ‡©πŸ‡ͺ Germany
  • Country Total Rank 3,151
  • Country Ranking
    Python
         152
    Shell
         741
    Go
         2,327

Top repositories

1

IMS-Toucan

Multilingual and Controllable Text-to-Speech Toolkit of the Speech and Language Technologies Group at the University of Stuttgart.
Python
1,367
star
2

adviser

ADvISER is a flexible framework to encourage task-oriented dialog system research & development
Python
57
star
3

VoicePAT

VoicePAT is a modular and efficient toolkit for voice privacy research, with main focus on speaker anonymization.
Shell
46
star
4

speaker-anonymization

Speaker anonymization pipeline for hiding the identity of the speaker of a recording by changing the voice in it.
Python
41
star
5

reading-comprehension

Comparing attention-based convolutional and recurrent neural networks under adversarial attacks to investigate their success and limitations in machine reading comprehension
Python
11
star
6

cyclegan-emotion-transfer

CycleGAN-based Emotion Style Transfer as Data Augmentation for Speech Emotion Recognition
Python
11
star
7

conversational-tree-search

Code and Data for Conversational Tree Search: A new task that bridges the gap between FAQ-style information retrieval and task-oriented dialog.
Python
5
star
8

IMS-Speech

IMS-Speech is a tool for German, English and Russian speech transcription aiming to facilitate research in various disciplines. We are willing to provide a speech transcription service with an intuitive web interface accessible with a wide range of computing devices and to people with various backgrounds. Our service is available here: https://75474978-c3fa-43a5-aa6c-ee36f2513064.ma.bw-cloud-instance.org/ims-speech/
Go
5
star
9

nlg-eval

Code accompanying the INLG 2018 paper Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity
Python
5
star
10

Intrinsic-Subgraph-Generation-for-VQA

Predicting a subgraph alongside the answer in a graph based VQA model
Python
5
star
11

hard-negative-captions

Python
4
star
12

diagraph

DIAGRAPH: An open-source graphic interface for dialog flow design
Python
4
star
13

multilingual-seq2seq-slu

Materials for the publication "Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding"
Python
2
star
14

BetterFinetuning

Code accompanying our paper on finetuning self-supervised general speech representations with a combination of contrastive and non-contrastive methods.
Python
1
star