Sivanand Achanta, Rambabu Banoth, Ayushi Pandey, Anandaswarup Vadapalli, S. Gangashetty
In this paper, we propose to use hidden state vector ob-tained from recurrent neural network (RNN) as a context vector representation for deep neural network (DNN) based statistical parametric speech synthesis. While in a typical DNN based system, there is a hierarchy of text features from phone level to utterance level, they are usually in 1-hot-k encoded representation. Our hypothesis is that, supplementing the conventional text features with a continuous frame-level acoustically guided representation would improve the acoustic modeling. The hidden state from an RNN trained to predict acoustic features is used as the additional contextual information. A dataset consisting of 2 Indian languages (Telugu and Hindi) from Blizzard challenge 2015 was used in our experiments. Both the subjective listening tests and the objective scores indicate that the proposed approach per-forms significantly better than the baseline DNN system.
{"title":"Contextual Representation using Recurrent Neural Network Hidden State for Statistical Parametric Speech Synthesis","authors":"Sivanand Achanta, Rambabu Banoth, Ayushi Pandey, Anandaswarup Vadapalli, S. Gangashetty","doi":"10.21437/SSW.2016-28","DOIUrl":"https://doi.org/10.21437/SSW.2016-28","url":null,"abstract":"In this paper, we propose to use hidden state vector ob-tained from recurrent neural network (RNN) as a context vector representation for deep neural network (DNN) based statistical parametric speech synthesis. While in a typical DNN based system, there is a hierarchy of text features from phone level to utterance level, they are usually in 1-hot-k encoded representation. Our hypothesis is that, supplementing the conventional text features with a continuous frame-level acoustically guided representation would improve the acoustic modeling. The hidden state from an RNN trained to predict acoustic features is used as the additional contextual information. A dataset consisting of 2 Indian languages (Telugu and Hindi) from Blizzard challenge 2015 was used in our experiments. Both the subjective listening tests and the objective scores indicate that the proposed approach per-forms significantly better than the baseline DNN system.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129464246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Andersson, S. Berlin, André Costa, Harald Berthelsen, Hanna Lindgren, N. Lindberg, J. Beskow, Jens Edlund, Joakim Gustafson
We present WikiSpeech, an ambitious joint project aiming to (1) make open source text-to-speech available through Wikimedia Foundation’s server architecture; (2) utilize the large and active Wikipedia user base to achieve continuously improving text-to-speech; (3) improve existing and develop new crowdsourcing methods for text-to-speech; and (4) develop new and adapt current evaluation methods so that they are well suited for the particular use case of reading Wikipedia articles out loud while at the same time capable of harnessing the huge user base made available by Wikipedia. At its inauguration, the project is backed by The Swedish Post and Telecom Authority and headed by Wikimedia Sverige, STTS and KTH, but in the long run, the project aims at broad multinational involvement. The vision of the project is freely available text-to-speech for all Wikipedia languages (currently 293). In this paper, we present the project itself and its first steps: requirements, initial architecture, and initial steps to include crowdsourcing and evaluation.
{"title":"WikiSpeech - enabling open source text-to-speech for Wikipedia","authors":"J. Andersson, S. Berlin, André Costa, Harald Berthelsen, Hanna Lindgren, N. Lindberg, J. Beskow, Jens Edlund, Joakim Gustafson","doi":"10.21437/SSW.2016-16","DOIUrl":"https://doi.org/10.21437/SSW.2016-16","url":null,"abstract":"We present WikiSpeech, an ambitious joint project aiming to (1) make open source text-to-speech available through Wikimedia Foundation’s server architecture; (2) utilize the large and active Wikipedia user base to achieve continuously improving text-to-speech; (3) improve existing and develop new crowdsourcing methods for text-to-speech; and (4) develop new and adapt current evaluation methods so that they are well suited for the particular use case of reading Wikipedia articles out loud while at the same time capable of harnessing the huge user base made available by Wikipedia. At its inauguration, the project is backed by The Swedish Post and Telecom Authority and headed by Wikimedia Sverige, STTS and KTH, but in the long run, the project aims at broad multinational involvement. The vision of the project is freely available text-to-speech for all Wikipedia languages (currently 293). In this paper, we present the project itself and its first steps: requirements, initial architecture, and initial steps to include crowdsourcing and evaluation.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123949288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew Wilkinson, A. Parlikar, Sunayana Sitaram, Tim White, A. Black, Suresh Bazaj
Open-source text-to-speech (TTS) software has enabled the development of voices in multiple languages, including many high-resource languages, such as English and European languages. However, building voices for low-resource languages is still challenging. We describe the development of TTS systems for 12 Indian languages using the Festvox framework, for which we developed a common frontend for Indian languages. Voices for eight of these 12 languages are available for use with Flite, a lightweight, fast run-time synthesizer, and the Android Flite app available in the Google Play store. Recently, the baseline Punjabi TTS voice was built end-to-end in a month by two undergraduate students (without any prior knowledge of TTS) with help from two of the authors of this paper. The framework can be used to build a baseline Indic TTS voice in two weeks, once a text corpus is selected and a suitable native speaker is identified.
{"title":"Open-Source Consumer-Grade Indic Text To Speech","authors":"Andrew Wilkinson, A. Parlikar, Sunayana Sitaram, Tim White, A. Black, Suresh Bazaj","doi":"10.21437/SSW.2016-31","DOIUrl":"https://doi.org/10.21437/SSW.2016-31","url":null,"abstract":"Open-source text-to-speech (TTS) software has enabled the development of voices in multiple languages, including many high-resource languages, such as English and European languages. However, building voices for low-resource languages is still challenging. We describe the development of TTS systems for 12 Indian languages using the Festvox framework, for which we developed a common frontend for Indian languages. Voices for eight of these 12 languages are available for use with Flite, a lightweight, fast run-time synthesizer, and the Android Flite app available in the Google Play store. Recently, the baseline Punjabi TTS voice was built end-to-end in a month by two undergraduate students (without any prior knowledge of TTS) with help from two of the authors of this paper. The framework can be used to build a baseline Indic TTS voice in two weeks, once a text corpus is selected and a suitable native speaker is identified.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114595027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents a study on the suitability of prosodic andacoustic features, with a special focus on i-vectors, in expressivespeech analysis and synthesis. For each utterance of two dif-ferent databases, a laboratory recorded emotional acted speech,and an audiobook, several prosodic and acoustic features are ex-tracted. Among them, i-vectors are built not only on the MFCCbase, but also on F0, power and syllable durations. Then, un-supervised clustering is performed using different feature com-binations. The resulting clusters are evaluated calculating clus-ter entropy for labeled portions of the databases. Additionally,synthetic voices are trained, applying speaker adaptive training,from the clusters built from the audiobook. The voices are eval-uated in a perceptual test where the participants have to edit anaudiobook paragraph using the synthetic voices.The objective results suggest that i-vectors are very use-ful for the audiobook, where different speakers (book charac-ters) are imitated. On the other hand, for the laboratory record-ings, traditional prosodic features outperform i-vectors. Also,a closer analysis of the created clusters suggest that differentspeakers use different prosodic and acoustic means to conveyemotions. The perceptual results suggest that the proposed i-vector based feature combinations can be used for audiobookclustering and voice training.
{"title":"Prosodic and Spectral iVectors for Expressive Speech Synthesis","authors":"Igor Jauk, A. Bonafonte","doi":"10.21437/SSW.2016-10","DOIUrl":"https://doi.org/10.21437/SSW.2016-10","url":null,"abstract":"This work presents a study on the suitability of prosodic andacoustic features, with a special focus on i-vectors, in expressivespeech analysis and synthesis. For each utterance of two dif-ferent databases, a laboratory recorded emotional acted speech,and an audiobook, several prosodic and acoustic features are ex-tracted. Among them, i-vectors are built not only on the MFCCbase, but also on F0, power and syllable durations. Then, un-supervised clustering is performed using different feature com-binations. The resulting clusters are evaluated calculating clus-ter entropy for labeled portions of the databases. Additionally,synthetic voices are trained, applying speaker adaptive training,from the clusters built from the audiobook. The voices are eval-uated in a perceptual test where the participants have to edit anaudiobook paragraph using the synthetic voices.The objective results suggest that i-vectors are very use-ful for the audiobook, where different speakers (book charac-ters) are imitated. On the other hand, for the laboratory record-ings, traditional prosodic features outperform i-vectors. Also,a closer analysis of the created clusters suggest that differentspeakers use different prosodic and acoustic means to conveyemotions. The perceptual results suggest that the proposed i-vector based feature combinations can be used for audiobookclustering and voice training.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130595296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a method for making nonaudible murmur (NAM) enhancement based on statistical voice conversion (VC) robust against external noise. NAM, which is an extremely soft whispered voice, is a promising medium for silent speech communication thanks to its faint volume. Although such a soft voice can still be detected with a special body-conductive microphone, its quality significantly degrades compared to that of air-conductive voices. It has been shown that the statistical VC technique is capable of significantly improving quality of NAM by converting it into the air-conductive voices. However, this technique is not helpful under noisy conditions because a detected NAM signal easily suffers from external noise, and acoustic mismatches are caused between such a noisy NAM signal and a previously trained conversion model. To address this issue, in this paper we apply our proposed noise suppression method based on external noise monitoring to the statistical NAM enhancement. Moreover, a known noise superimposition method is further applied in order to alleviate the effects of residual noise components on the conversion accuracy. The experimental results demonstrate that the proposed method yields significant improvements in the conversion accuracy compared to the conventional method.
{"title":"Nonaudible murmur enhancement based on statistical voice conversion and noise suppression with external noise monitoring","authors":"Y. Tajiri, T. Toda","doi":"10.21437/SSW.2016-9","DOIUrl":"https://doi.org/10.21437/SSW.2016-9","url":null,"abstract":"This paper presents a method for making nonaudible murmur (NAM) enhancement based on statistical voice conversion (VC) robust against external noise. NAM, which is an extremely soft whispered voice, is a promising medium for silent speech communication thanks to its faint volume. Although such a soft voice can still be detected with a special body-conductive microphone, its quality significantly degrades compared to that of air-conductive voices. It has been shown that the statistical VC technique is capable of significantly improving quality of NAM by converting it into the air-conductive voices. However, this technique is not helpful under noisy conditions because a detected NAM signal easily suffers from external noise, and acoustic mismatches are caused between such a noisy NAM signal and a previously trained conversion model. To address this issue, in this paper we apply our proposed noise suppression method based on external noise monitoring to the statistical NAM enhancement. Moreover, a known noise superimposition method is further applied in order to alleviate the effects of residual noise components on the conversion accuracy. The experimental results demonstrate that the proposed method yields significant improvements in the conversion accuracy compared to the conventional method.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114400296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The effortless speech production in humans requires coordinated movements of the articulators such as lips, tongue, jaw, velum, etc. Therefore, measured trajectories obtained are smooth and slowly-varying. However, the trajectories estimated from acoustic-to-articulatory inversion (AAI) are found to be jagged . Thus, energy minimization is used as smoothness constraint for improving performance of the AAI. Besides energy minimization, jerk (i.e., rate of change of acceleration) is known for quantification of smoothness in case of human motor movements. Human motors are organized to achieve intended goal with smoothest possible movements, under the constraint of minimum accelerative transients. In this paper, we propose jerk minimization as an alternative smoothness criterion for frame-based acoustic-to-articulatory inversion. The resultant trajectories obtained are smooth in the sense that for articulator-specific window size, they will have minimum jerk. The results using this criterion were found to be comparable with inversion schemes based on existing energy minimization criteria for achieving smoothness.
{"title":"Jerk Minimization for Acoustic-To-Articulatory Inversion","authors":"Avni Rajpal, H. Patil","doi":"10.21437/SSW.2016-14","DOIUrl":"https://doi.org/10.21437/SSW.2016-14","url":null,"abstract":"The effortless speech production in humans requires coordinated movements of the articulators such as lips, tongue, jaw, velum, etc. Therefore, measured trajectories obtained are smooth and slowly-varying. However, the trajectories estimated from acoustic-to-articulatory inversion (AAI) are found to be jagged . Thus, energy minimization is used as smoothness constraint for improving performance of the AAI. Besides energy minimization, jerk (i.e., rate of change of acceleration) is known for quantification of smoothness in case of human motor movements. Human motors are organized to achieve intended goal with smoothest possible movements, under the constraint of minimum accelerative transients. In this paper, we propose jerk minimization as an alternative smoothness criterion for frame-based acoustic-to-articulatory inversion. The resultant trajectories obtained are smooth in the sense that for articulator-specific window size, they will have minimum jerk. The results using this criterion were found to be comparable with inversion schemes based on existing energy minimization criteria for achieving smoothness.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117138581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with sin- gle speaker model. Moreover, we also tackle the problem of speaker interpolation by adding a new output layer (a-layer) on top of the multi-output branches. An identifying code is injected into the layer together with acoustic features of many speakers. Experiments show that the a-layer can effectively learn to interpolate the acoustic features between speakers.
{"title":"Multi-output RNN-LSTM for multiple speaker speech synthesis with α-interpolation model","authors":"Santiago Pascual, A. Bonafonte","doi":"10.21437/SSW.2016-19","DOIUrl":"https://doi.org/10.21437/SSW.2016-19","url":null,"abstract":"Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with sin- \u0000gle speaker model. Moreover, we also tackle the problem of speaker interpolation by adding a new output layer (a-layer) on top of the multi-output branches. An identifying code is injected into the layer together with acoustic features of many speakers. Experiments show that the a-layer can effectively learn to interpolate the acoustic features between speakers.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a new quality assessment method for synthesized speech. Unlike previous approaches which uses Hidden Markov Model (HMM) trained on natural utterances as a reference model to predict the quality of synthesized speech, proposed approach uses knowledge about synthesized speech while training the model. The previous approach has been successfully applied in the quality assessment of synthesized speech for the German language. However, it gave poor results for English language databases such as Blizzard Challenge 2008 and 2009 databases. The problem of quality assessment of synthesized speech is posed as a regression problem. The mapping between statistical properties of spectral features extracted from the speech signal and corresponding speech quality score (MOS) was found using Support Vector Regression (SVR). All the experiments were done on Blizzard Challenge Databases of the year 2008, 2009, 2010 and 2012. The results of experiments show that by including knowledge about synthesized speech while training, the performance of quality assessment system can be improved. Moreover, the accuracy of quality assessment system heavily depends on the kind of synthesis system used for signal generation. On Blizzard 2008 and 2009 database, proposed approach gives correlation of 0.28 and 0.49 , respectively, for about 17 % data used in training. Previous approach gives correlation of 0.3 and 0.09 , respectively, using spectral features. For Blizzard 2012 database, proposed approach gives correlation of 0.8 by using 12 % of available data in training.
{"title":"Non-intrusive Quality Assessment of Synthesized Speech using Spectral Features and Support Vector Regression","authors":"Meet H. Soni, H. Patil","doi":"10.21437/SSW.2016-21","DOIUrl":"https://doi.org/10.21437/SSW.2016-21","url":null,"abstract":"In this paper, we propose a new quality assessment method for synthesized speech. Unlike previous approaches which uses Hidden Markov Model (HMM) trained on natural utterances as a reference model to predict the quality of synthesized speech, proposed approach uses knowledge about synthesized speech while training the model. The previous approach has been successfully applied in the quality assessment of synthesized speech for the German language. However, it gave poor results for English language databases such as Blizzard Challenge 2008 and 2009 databases. The problem of quality assessment of synthesized speech is posed as a regression problem. The mapping between statistical properties of spectral features extracted from the speech signal and corresponding speech quality score (MOS) was found using Support Vector Regression (SVR). All the experiments were done on Blizzard Challenge Databases of the year 2008, 2009, 2010 and 2012. The results of experiments show that by including knowledge about synthesized speech while training, the performance of quality assessment system can be improved. Moreover, the accuracy of quality assessment system heavily depends on the kind of synthesis system used for signal generation. On Blizzard 2008 and 2009 database, proposed approach gives correlation of 0.28 and 0.49 , respectively, for about 17 % data used in training. Previous approach gives correlation of 0.3 and 0.09 , respectively, using spectral features. For Blizzard 2012 database, proposed approach gives correlation of 0.8 by using 12 % of available data in training.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhengchen Zhang, Fuxiang Wu, Chenyu Yang, M. Dong, Fu-qiu Zhou
Prosodic phrases (PPs) are important for Mandarin Text-To-Speech systems. Most of the existing PP detection methods need large manually annotated corpora to learn the models. In this paper, we propose a rule based method to predict the PP boundaries employing the syntactic information of a sentence. The method is based on the ob-servation that a prosodic phrase is a meaningful segment of a sentence with length restrictions. A syntactic structure allows to segment a sentence according to grammars. We add some length restrictions to the segmentations to predict the PP boundaries. An F-Score of 0.693 was obtained in the experiments, which is about 0.02 higher than the one got by a Conditional Random Field based method.
{"title":"Mandarin Prosodic Phrase Prediction based on Syntactic Trees","authors":"Zhengchen Zhang, Fuxiang Wu, Chenyu Yang, M. Dong, Fu-qiu Zhou","doi":"10.21437/SSW.2016-26","DOIUrl":"https://doi.org/10.21437/SSW.2016-26","url":null,"abstract":"Prosodic phrases (PPs) are important for Mandarin Text-To-Speech systems. Most of the existing PP detection methods need large manually annotated corpora to learn the models. In this paper, we propose a rule based method to predict the PP boundaries employing the syntactic information of a sentence. The method is based on the ob-servation that a prosodic phrase is a meaningful segment of a sentence with length restrictions. A syntactic structure allows to segment a sentence according to grammars. We add some length restrictions to the segmentations to predict the PP boundaries. An F-Score of 0.693 was obtained in the experiments, which is about 0.02 higher than the one got by a Conditional Random Field based method.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126297231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper discusses non-filter waveform generation from cepstral features using spectral phase reconstruction as an alternative method to replace the conventional source-filter model in text-to-speech (TTS) systems. As the primary purpose of the use of filters is considered as producing a waveform from the desired spectrum shape, one possible alternative of the source-filter framework is to directly convert the designed spectrum into a waveform by utilizing a recently developed “ phase reconstruction ” from the power spectrogram. Given cepstral features and fundamental frequency ( F 0 ) as desired spectrum from a TTS system, the spectrum to be heard by the listener is calculated by converting the cepstral features into a linear-scale power spectrum and multiplying with the pitch structure of F 0 . The signal waveform is generated from the power spectrogram by spectral phase reconstruction. An advantageous property of the proposed method is that it is free from undesired amplitude and long time decay often caused by sharp resonances in recursive filters. In preliminary experiments, we compared temporal and gain characteristics of the synthesized speech using the proposed method and mel-log spectrum approximation (MLSA) filter. Results show the proposed method performed better than the MLSA filter in the both characteristics of the synthesized speech, and imply a desirable properties of the proposed method for speech synthesis.
{"title":"Non-filter waveform generation from cepstrum using spectral phase reconstruction","authors":"Yasuhiro Hamada, Nobutaka Ono, S. Sagayama","doi":"10.21437/SSW.2016-5","DOIUrl":"https://doi.org/10.21437/SSW.2016-5","url":null,"abstract":"This paper discusses non-filter waveform generation from cepstral features using spectral phase reconstruction as an alternative method to replace the conventional source-filter model in text-to-speech (TTS) systems. As the primary purpose of the use of filters is considered as producing a waveform from the desired spectrum shape, one possible alternative of the source-filter framework is to directly convert the designed spectrum into a waveform by utilizing a recently developed “ phase reconstruction ” from the power spectrogram. Given cepstral features and fundamental frequency ( F 0 ) as desired spectrum from a TTS system, the spectrum to be heard by the listener is calculated by converting the cepstral features into a linear-scale power spectrum and multiplying with the pitch structure of F 0 . The signal waveform is generated from the power spectrogram by spectral phase reconstruction. An advantageous property of the proposed method is that it is free from undesired amplitude and long time decay often caused by sharp resonances in recursive filters. In preliminary experiments, we compared temporal and gain characteristics of the synthesized speech using the proposed method and mel-log spectrum approximation (MLSA) filter. Results show the proposed method performed better than the MLSA filter in the both characteristics of the synthesized speech, and imply a desirable properties of the proposed method for speech synthesis.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122312985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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