Search

application/pdf

application/pdf

Este artículo describe el proceso de generación de una voz en castellano utilizando el corpus UPC ESMA de UPC proporcionado por la Evaluación Albayzín 2008: Conversión Texto a Voz. Se ha implementado una voz basada en selección de unidades mediante el paquete Multisyn de Festival y otra basada en Hidden Semi-Markov Models (HSMM) mediante HTS. Tras una breve evaluación de la calidad de ambas voces, se detallan las características principales de la voz basada en HSMM, sistema final presentado a la evaluación.

One of the biggest challenges in speech synthesis is the production of naturally sounding synthetic voices. This means that the resulting voice must be not only of high enough quality but also that it must be able to capture the natural expressiveness imbued in human speech. This paper focus on solving the expressiveness problem by proposing a set of different techniques that could be used for extrapolating the expressiveness of proven high quality speaking style models into neutral speakers in HMM-based synthesis. As an additional advantage, the proposed techniques are based on adaptation approaches, which means that they can be used with little training data (around 15 minutes of training data are used in each style for this pa- per). For the final implementation, a set of 4 speaking styles were considered: news broadcasts, live sports commentary, interviews and parliamentary speech. Finally, the implementation of the 5 techniques were tested through a perceptual evaluation that proves that the deviations between neutral and speaking style average models can be learned and used to imbue expressiveness into target neutral speakers as intended.

When designing human-machine interfaces it is important to consider not only the bare bones functionality but also the ease of use and accessibility it provides. When talking about voice-based inter- faces, it has been proven that imbuing expressiveness into the synthetic voices increases signi?cantly its perceived naturalness, which in the end is very helpful when building user friendly interfaces. This paper proposes an adaptation based expressiveness transplantation system capable of copying the emotions of a source speaker into any desired target speaker with just a few minutes of read speech and without requiring the record- ing of additional expressive data. This system was evaluated through a perceptual test for 3 speakers showing up to an average of 52% emotion recognition rates relative to the natural voice recognition rates, while at the same time keeping good scores in similarity and naturality.

Current text-to-speech systems are developed using studio-recorded speech in a neutral style or based on acted emotions. However, the proliferation of media sharing sites would allow developing a new generation of speech-based systems which could cope with spontaneous and styled speech. This paper proposes an architecture to deal with realistic recordings and carries out some experiments on unsupervised speaker diarization. In order to maximize the speaker purity of the clusters while keeping a high speaker coverage, the paper evaluates the F-measure of a diarization module, achieving high scores (>85%) especially when the clusters are longer than 30 seconds, even for he more spontaneous and expressive styles (such as talk shows or sports).

Synthetic speech can be modified to improve intelligibility in noise. In order to perform modifications automatically, it would be useful to have an objective measure that could predict the intelligibility of modified synthetic speech for human listeners. We analysed the impact on intelligibility - and on how well objective measures predict it -“ when we separately modify speaking rate, fundamental frequency, line spectral pairs and spectral peaks. Shifting LSPs can increase intelligibility for human listeners; other modifications had weaker effects. Among the objective measures we evaluated, the Dau model and the Glimpse proportion were the best predictors of human performance.

In the EMIME project we have studied unsupervised cross-lingual speaker adaptation. We have employed an HMM statistical framework for both speech recognition and synthesis which provides transformation mechanisms to adapt the synthesized voice in TTS (text-to-speech) using the recognized voice in ASR (automatic speech recognition). An important application for this research is personalised speech-to-speech translation that will use the voice of the speaker in the input language to utter the translated sentences in the output language. In mobile environments this enhances the users' interaction across language barriers by making the output speech sound more like the original speaker's way of speaking, even if she or he could not speak the output language.

In this paper, we investigate imposture using synthetic speech. Although this problem was first examined over a decade ago, dramatic improvements in both speaker verification (SV) and speech synthesis have renewed interest in this problem. We use a HMM-based speech synthesizer which creates synthetic speech for a targeted speaker through adaptation of a background model. We use two SV systems: standard GMMUBM- based and a newer SVM-based. Our results show when the systems are tested with human speech, there are zero false acceptances and zero false rejections. However, when the systems are tested with synthesized speech, all claims for the targeted speaker are accepted while all other claims are rejected. We propose a two-step process for detection of synthesized speech in order to prevent this imposture. Overall, while SV systems have impressive accuracy, even with the proposed detector, high-quality synthetic speech will lead to an unacceptably high false acceptance rate.

APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: Infrastructure Software for Speech Processing (5 October 2009)., A statistical parametric approach to speech synthesis based HMMs has grown in popularity over the last few years. In this approach, spectrum, excitation, and duration of speech are simultaneously modeled by context-dependent HMMs, and speech waveforms are generated from the HMMs themselves. Since December 2002, we have publicly released an opensource software toolkit named "HMM-based speech synthesis system (HTS)" to provide a research and development toolkit for statistical parametric speech synthesis. This paper describes recent developments of HTS in detail, as well as future release plans.

Our recent experiments with HMM-based speech synthesis systems have demonstrated that speaker-adaptive HMM-based speech synthesis (which uses an 'average voice model' plus model adaptation) is robust to non-ideal speech data that are recorded under various conditions and with varying microphones, that are not perfectly clean, and/or that lack of phonetic balance. This enables us consider building high-quality voices on 'non-TTS' corpora such as ASR corpora. Since ASR corpora generally include a large number of speakers, this leads to the possibility of producing an enormous number of voices automatically. In this paper we show thousands of voices for HMM-based speech synthesis that we have made from several popular ASR corpora such as the Wall Street Journal databases (WSJ0/WSJ1/WSJCAM0), Resource Management, Globalphone and Speecon. We report some perceptual evaluation results and outline the outstanding issues.