Paper Info
Title
Weakly-Supervised Phrase Assignment From Text In A Speech-Synthesis System Using Noisy Labels
Abstract
The proper segmentation of an input text string into meaningful intonational phrase units is a fundamental task in the text processing component of a text-to-speech (TTS) system that generates intelligible and natural synthesis. In this work we look at the creation of a symbolic, phrase-assignment model within the front end (FE) of a North American English TTS system when high-quality labels for supervised learning are unavailable and/or potentially mismatched to the target corpus and domain. We explore a labeling scheme that merges heuristics derived from (i) automatic high-quality phonetic alignments, (ii) linguistic rules. and (iii) a legacy acoustic phrase-labeling system to arrive at a ground truth that can be used to train a bidirectional recurrent neural network model. We evaluate the performance of this model in terms of objective metrics describing categorical phrase assignment within the FE proper, as well as on the effect that these intermediate labels carry onto the TTS back end for the task of continuous prosody prediction (i.e., intonation and duration contours, and pausing). For this second task, we rely on subjective listening tests and demonstrate that the proposed system significantly outperforms a linguistic rules based baseline for two different synthetic voices.
Year
DOI
Venue
2017
10.21437/Interspeech.2017-487
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION
Keywords
Field
DocType
speech synthesis, phrasing, prosody modeling, recurrent neural networks
Speech synthesis,Computer science,Phrase,Speech recognition,Natural language processing,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2308-457X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Asaf Rendel1383.08
Raul Fernandez229964.85
Zvi Kons3204.79
Andrew Rosenberg4122.53
Ron Hoory518119.16
Bhuvana Ramabhadran61779153.83