AutoBI - a tool for automatic toBI annotation

By Andrew Rosenberg. 2010

172 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Lei He
6. Lei Xie
7. Yu Zhang
8. Ye Jia

In Experimental and Cognitive Psychology

1. R. Skerry-Ryan
2. Tao Qin
3. Lei Xie
4. R. Clark
5. Ying Xiao
6. Frank K. Soong
7. Piero Cosi
8. Tie-Yan Liu

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “For example, the prosody information can be labeled according to some annotation schemas, such as ToBI [301], AuToBI [288], Tilt [339], INTSINT [109], and SLAM [243].”
• Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron (2018, methodology): “Numerous works have explored annotation schemes for diagramming and automatic labeling of prosody: ToBi (Silverman et al., 1992), AuToBI (Rosenberg, 2010), Tilt (Taylor, 1998), INTSINT (Hirst, 2001), SLAM (Obin et al., 2014) all describe methods for the annotation and automatic extraction of labels ”
• Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis (2018, methodology): “AuToBI, for example, (Rosenberg, 2010) aims to produce ToBI (Silverman et al., 1992) labels that can be used by other TTS models.”
• Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS (2019, methodology): “Speech prosody of the training data can be annotated in ToBI [9] manually or automatically [10] first to train a ToBI prosody model for predicting ToBI labels from the given text.”
• Predicting Expressive Speaking Style from Text in End-To-End Speech Synthesis (2018, unknown): “Notable among these is AuToBI [9], which au- ar X iv :1 80 8.”
• Intent transfer in speech-to-speech machine translation (2012, unknown): “In order to efficiently and scalably establish the above result without manual annotation, we employ the accentedness detection module of AuToBI, on each language pair in both directions.”
• Experiments with signal-driven symbolic prosody for statistical parametric speech synthesis (2013, methodology): “The accuracy of the detection and classification tasks has been evaluated in [5], reporting good results for pitch accent and satisfactory results for phrase boundaries.”