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Non-Linear Pitch Modification in Voice Conversion using Artificial Neural Networks
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0003-1399-6604
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0002-0397-6442
2013 (English)In: Advances in nonlinear speech processing: 6th International Conference, NOLISP 2013, Mons, Belgium, June 19-21, 2013 : proceedings, Springer Berlin/Heidelberg, 2013, 97-103 p.Conference paper, Published paper (Refereed)
Abstract [en]

Majority of the current voice conversion methods do not focus on the modelling local variations of pitch contour, but only on linear modification of the pitch values, based on means and standard deviations. However, a significant amount of speaker related information is also present in pitch contour. In this paper we propose a non-linear pitch modification method for mapping the pitch contours of the source speaker according to the target speaker pitch contours. This work is done within the framework of Artificial Neural Networks (ANNs) based voice conversion. The pitch contours are represented with Discrete Cosine Transform (DCT) coefficients at the segmental level. The results evaluated using subjective and objective measures confirm that the proposed method performed better in mimicking the target speaker's speaking style when compared to the linear modification method.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. 97-103 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7911
Keyword [en]
Discrete cosine transform coefficients, Local variations, Modification methods, Pitch modification, Speaking styles, Standard deviation, Subjective and objective measures, Voice conversion
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-137386Scopus ID: 2-s2.0-84888246669OAI: oai:DiVA.org:kth-137386DiVA: diva2:678924
Conference
6th International Conference on Advances in Nonlinear Speech Processing, NOLISP 2013; Mons; Belgium; 19 June 2013 through 21 June 2013
Note

QC 20140116

Available from: 2013-12-13 Created: 2013-12-13 Last updated: 2017-04-28Bibliographically approved
In thesis
1. Towards conversational speech synthesis: Experiments with data quality, prosody modification, and non-verbal signals
Open this publication in new window or tab >>Towards conversational speech synthesis: Experiments with data quality, prosody modification, and non-verbal signals
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of a text-to-speech synthesis (TTS) system is to generate a human-like speech waveform from a given input text. Current TTS sys- tems have already reached a high degree of intelligibility, and they can be readily used to read aloud a given text. For many applications, e.g. public address systems, reading style is enough to convey the message to the people. However, more recent applications, such as human-machine interaction and speech-to-speech translation, call for TTS systems to be increasingly human- like in their conversational style. The goal of this thesis is to address a few issues involved in a conversational speech synthesis system.

First, we discuss issues involve in data collection for conversational speech synthesis. It is very important to have data with good quality as well as con- tain more conversational characteristics. In this direction we studied two methods 1) harvesting the world wide web (WWW) for the conversational speech corpora, and 2) imitation of natural conversations by professional ac- tors. In former method, we studied the effect of compression on the per- formance of TTS systems. It is often the case that speech data available on the WWW is in compression form, mostly use the standard compression techniques such as MPEG. Thus in paper 1 and 2, we systematically stud- ied the effect of MPEG compression on TTS systems. Results showed that the synthesis quality indeed affect by the compression, however, the percep- tual differences are strongly significant if the compression rate is less than 32kbit/s. Even if one is able to collect the natural conversational speech it is not always suitable to train a TTS system due to problems involved in its production. Thus in later method, we asked the question that can we imi- tate the conversational speech by professional actors in recording studios. In this direction we studied the speech characteristics of acted and read speech. Second, we asked a question that can we borrow a technique from voice con- version field to convert the read speech into conversational speech. In paper 3, we proposed a method to transform the pitch contours using artificial neu- ral networks. Results indicated that neural networks are able to transform pitch values better than traditional linear approach. Finally, we presented a study on laughter synthesis, since non-verbal sounds particularly laughter plays a prominent role in human communications. In paper 4 we present an experimental comparison of state-of-the-art vocoders for the application of HMM-based laughter synthesis. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. 39 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:04
Keyword
Speech synthesis, MPEG compression, Voice Conversion, Artificial Neural Net- works, Laughter synthesis, HTS
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-198100 (URN)978-91-7729-235-7 (ISBN)
Presentation
2017-01-19, Fantum, Lindstedtsvägen 24, 5tr, Stockholm, 15:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2013-4935
Note

QC 20161213

Available from: 2016-12-19 Created: 2016-12-12 Last updated: 2016-12-19Bibliographically approved

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Beskow, JonasGustafson, Joakim

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