A novel quality assessment system design for closed captioning (CC) is proposed. CC was originally designed to serve hearing-impaired audiences. Traditional quality assessment models focus on empirical methods only, measuring quantitative accuracy by counting the number of word errors in the captions for a segment. However, hearing-impaired audiences have been outspoken about their dissatisfaction with the quality of current CC. One solution to this problem may involve inviting human […]

Deep learning-based text to speech (TTS) is used in various situations, and the sound quality is close to that of humans. We previously developed a news-specific deep learning-based TTS (DL-TTS) system and implemented it with our AI news anchor for live broadcast programs and automatic news-speech distribution services. We also developed our DL-TTS system for the control of speaking style and speech rate, pitch, intonation, and volume to facilitate the creation of various programs. More […]

Sports broadcasters and streaming platforms are continuously seeking new ways to engage fans and to deliver immersive experiences that bring them closer to realtime action. To gain speed and efficiency, and to create new revenue opportunities, live sports producers are now exploring innovative technologies, with artificial intelligence (AI) and machine learning (ML) at the forefront.

Casting voice-actors to dub source language content into a target language—known as voice casting—consists largely of a manual workflow that could benefit immensely from increased levels of automation. Recent advancements in deep learning architectures for sequential data processing are providing the needed impetus to the realization of various AI-enabled audio-processing workflows. Specifically, applications such as speaker verification and speech synthesis have been gaining immense […]