Multi-Stage Audio-Visual Fusion for Dysarthric Speech Recognition With Pre-Trained Models.

Dysarthric speech recognition helps speakers with dysarthria to enjoy better communication. However, collecting dysarthric speech is difficult. The machine learning models cannot be trained sufficiently using dysarthric speech.

Improvement of Acoustic Models Fused with Lip Visual Information for Low-Resource Speech.

Endangered language generally has low-resource characteristics, as an immaterial cultural resource that cannot be renewed. Automatic speech recognition (ASR) is an effective means to protect this language. However, for low-resource language, native speakers are few and labeled corpora are insufficient.

Mandarin Electrolaryngeal Speech Recognition Based on WaveNet-CTC.

Purpose The application of Chinese Mandarin electrolaryngeal (EL) speech for laryngectomees has been limited by its drawbacks such as single fundamental frequency, mechanical sound, and large radiation noise. To improve the intelligibility of Chinese Mandarin EL speech, a new perspective using the automatic speech recognition (ASR) system was proposed, which can convert EL speech into healthy speech, if combined with text-to-speech. Method An ASR system was designed to recognize EL speech based on a deep learning model WaveNet and the connectionist temporal classification (WaveNet-CTC).

Design and Preliminary Evaluation of Electrolarynx With F0 Control Based on Capacitive Touch Technology.

An electrolarynx (EL) is one of the most popular voice rehabilitation technologies used after laryngectomy. However, most ELs generate monotonic EL speech, which has been shown to create a particular deficit in speech intelligibility, especially for Chinese Mandarin (Mandarin). Mandarin is a tonal language that makes lexical distinctions using variations in tone.