916 research outputs found
LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices
Recent developments in speech synthesis have produced systems capable of
outcome intelligible speech, but now researchers strive to create models that
more accurately mimic human voices. One such development is the incorporation
of multiple linguistic styles in various languages and accents.
HMM-based Speech Synthesis is of great interest to many researchers, due to
its ability to produce sophisticated features with small footprint. Despite
such progress, its quality has not yet reached the level of the predominant
unit-selection approaches that choose and concatenate recordings of real
speech. Recent efforts have been made in the direction of improving these
systems.
In this paper we present the application of Long-Short Term Memory Deep
Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order
to obtain closer spectral characteristics to those of natural speech. The
results show how HMM-voices could be improved using this approach.Comment: 5 pages, 5 figure
BiSinger: Bilingual Singing Voice Synthesis
Although Singing Voice Synthesis (SVS) has made great strides with
Text-to-Speech (TTS) techniques, multilingual singing voice modeling remains
relatively unexplored. This paper presents BiSinger, a bilingual pop SVS system
for English and Chinese Mandarin. Current systems require separate models per
language and cannot accurately represent both Chinese and English, hindering
code-switch SVS. To address this gap, we design a shared representation between
Chinese and English singing voices, achieved by using the CMU dictionary with
mapping rules. We fuse monolingual singing datasets with open-source singing
voice conversion techniques to generate bilingual singing voices while also
exploring the potential use of bilingual speech data. Experiments affirm that
our language-independent representation and incorporation of related datasets
enable a single model with enhanced performance in English and code-switch SVS
while maintaining Chinese song performance. Audio samples are available at
https://bisinger-svs.github.io.Comment: Accepted by ASRU202
Query by Example of Speaker Audio Signals using Power Spectrum and MFCCs
Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%
Cross-Lingual Voice Conversion with Non-Parallel Data
In this project a Phonetic Posteriorgram (PPG) based Voice Conversion system is implemented. The main goal is to perform and evaluate conversions of singing voice. The cross-gender and cross-lingual scenarios are considered. Additionally, the use of spectral envelope based MFCC and pseudo-singing dataset for ASR training are proposed in order to improve the performance of the system in the singing context
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