Utilizing Wav2Vec In Database-Independent Voice Disorder Detection

Author:

Tirronen Saska1,Javanmardi Farhad1,Kodali Manila1,Reddy Kadiri Sudarsana1,Alku Paavo1

Affiliation:

1. Aalto University,Department of Information and Communications Engineering,Finland

Publisher

IEEE

Reference27 articles.

1. Wav2vec2-based Paralinguistic Systems to Recognise Vocalised Emotions and Stuttering

2. Arabic speech emotion recognition employing wav2vec2. 0 and hubert based on baved dataset;mohamed,2021

3. Introducing ecapa-tdnn and wav2vec2. 0 embeddings to stuttering detection;sheikh,2022

4. How does pre-trained wav2vec2. 0 perform on domain shifted asr? an extensive benchmark on air traffic control communications;zuluaga-gomez,2022

5. Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition

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