태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Predicting prosodic prominence from text. This is a 2-way classification task, assigning each word in a sentence a label 1 (prominent) or 0 (non-prominent).
<span style="color:grey; opacity: 0.6">( Image credit: Helsinki Prosody Corpus )</span>
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The CANDOR corpus is a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections.
The Helsinki Prosody Corpus is a dataset for predicting prosodic prominence from written text. The prosodic annotations are automatically generated, high quality prosodic for the 'clean' subsets of LibriTTS corpus (Zen et al., 2019), comprising of 262.5 hours of read speech from 1230 speakers. The transcribed sentences were aligned and then prosodically annotated with word-level acoustic prominence labels. Source: Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.