Combining vocal tract length normalization with hierarchial linear transformations

Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR-based adaptation techniques, being much closer in quality to that generated by the original av-erage voice model. However with only a single parameter, VTLN captures very few speaker specific characteristics when compared to linear transform based adaptation techniques. This paper pro-poses that the merits of VTLN can be combined with those of linear transform based adaptation in a hierarchial Bayesian frame-work, where VTLN is used as the prior information. A novel tech-nique for propagating the gender information from the VTLN prior through constrained structural maximum a posteriori linear regres-sion (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity. Index Terms — Statistical parametric speech synthesis, hidden Markov models, speaker adaptation, vocal tract length normaliza-tion, constrained structural maximum a posteriori linear regression 1

Combining Vocal Tract Length Normalization with Linear Transformations in a Bayesian Framework

Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR- based adaptation techniques, being much closer in quality to that generated by the original average voice model. By contrast, with just a single parameter, VTLN captures very few speaker specific characteristics when compared to the available linear transform based adaptation techniques. This paper proposes that the merits of VTLN can be combined with those of linear transform based adaptation technique in a Bayesian framework, where VTLN is used as the prior information. A novel technique of propa- gating the gender information from the VTLN prior through constrained structural maximum a posteriori linear regression (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity

Analysis of Speaker Adaptation Algorithms for HMM-based Speech Synthesis and a Constrained SMAPLR Adaptation Algorithm

In this paper we analyze the effects of several factors and configuration choices encountered during training and model construction when we want to obtain better and more stable adaptation in HMM-based speech synthesis. We then propose a new adaptation algorithm called constrained structural maximum a posteriori linear regression (CSMAPLR) whose derivation is based on the knowledge obtained in this analysis and on the results of comparing several conventional adaptation algorithms. Here we investigate six major aspects of the speaker adaptation: initial models transform functions, estimation criteria, and sensitivity of several linear regression adaptation algorithms algorithms. Analyzing the effect of the initial model, we compare speaker-dependent models, gender-independent models, and the simultaneous use of the gender-dependent models to single use of the gender-dependent models. Analyzing the effect of the transform functions, we compare the transform function for only mean vectors with that for mean vectors and covariance matrices. Analyzing the effect of the estimation criteria, we compare the ML criterion with a robust estimation criterion called structural MAP. We evaluate the sensitivity of several thresholds for the piecewise linear regression algorithms and take up methods combining MAP adaptation with the linear regression algorithms. We incorporate these adaptation algorithms into our speech synthesis system and present several subjective and objective evaluation results showing the utility and effectiveness of these algorithms in speaker adaptation for HMM-based speech synthesis

Expressive Speech Synthesis for Critical Situations

Presence of appropriate acoustic cues of affective features in the synthesized speech can be a prerequisite for the proper evaluation of the semantic content by the message recipient. In the recent work the authors have focused on the research of expressive speech synthesis capable of generating naturally sounding synthetic speech at various levels of arousal. Automatic information and warning systems can be used to inform, warn, instruct and navigate people in dangerous, critical situations, and increase the effectiveness of crisis management and rescue operations. One of the activities in the frame of the EU SF project CRISIS was called "Extremely expressive (hyper-expressive) speech synthesis for urgent warning messages generation''. It was aimed at research and development of speech synthesizers with high naturalness and intelligibility capable of generating messages with various expressive loads. The synthesizers will be applicable to generate public alert and warning messages in case of fires, floods, state security threats, etc. Early warning in relation to the situations mentioned above can be made thanks to fire and flood spread forecasting; modeling thereof is covered by other activities of the CRISIS project. The most important part needed for the synthesizer building is the expressive speech database. An original method is proposed to create such a database. The current version of the expressive speech database is introduced and first experiments with expressive synthesizers developed with this database are presented and discussed

Evaluation of a transplantation algorithm for expressive speech synthesis

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

Rejtett Markov-modell alapú szövegfelolvasó adaptációja félig spontán magyar beszéddel

Napjainkban számos automatikus szövegfelolvasási módszer létezik, de az elmúlt években a legnagyobb figyelmet a statisztikai parametrikus beszédkeltési módszer, ezen belül is a rejtett Markov-modell (Hidden Markov Model, HMM) alapú szövegfelolvasás kapta. A HMM-alapú szövegfelolvasás minsége megközelíti a manapság legjobbnak számító elemkiválasztásos szintézisét, és ezen túl számos elnnyel rendelkezik: adatbázisa kevés helyet foglal el, lehetséges új hangokat külön felvételek nélkül létrehozni, érzelmeket kifejezni vele, és már néhány mondatnyi felvétel esetén is lehetséges az adott beszél hangkarakterét visszaadni. Jelen cikkben bemutatjuk a HMM-alapú beszédkeltés alapjait, a beszéladaptációjának lehetségeit, a magyar nyelvre elkészült beszélfüggetlen HMM adatbázist és a beszéladaptáció folyamatát félig spontán magyar beszéd esetén. Az eredmények kiértékelése céljából meghallgatásos tesztet végzünk négy különböz hang adaptációja esetén, melyeket szintén ismertetünk a cikkünkben

Adaptation Experiments on French MediaParl ASR

This document summarizes adaptation experiments done on French MediaParl corpus and other French corpora. Baseline adaptation techniques are briefly presented and evaluated in the MediaParl task for speaker adaptation, speaker adaptive training, database combination and environmental adaptation. Results show that by applying baseline adaptation techniques, a relative WER reduction of up to 22.8% can be reached in French transcription accuracy. For the MediaParl task, performance of systems trained on directly merged databases and of systems trained on databases combined via MAP adaptation did not differ significantly when large amount of data was available. During the experiments, French data recorded in Switzerland behaved in a similar way compared to French data recorded in France, which suggest that French spoken in Valais is close to the standard French spoken in France, and differencies in ASR accuracies between models trained on Swiss MediaParl and on French BREF are more likely caused by environmental factors or more spontaneity in speech

VTLN-Based Rapid Cross-Lingual Adaptation for Statistical Parametric Speech Synthesis

Cross-lingual speaker adaptation (CLSA) has emerged as a new challenge in statistical parametric speech syn- thesis, with specific application to speech-to-speech translation. Recent research has shown that reasonable speaker similarity can be achieved in CLSA using maximum likelihood linear transformation of model parameters, but this method also has weaknesses due to the inherent mismatch caused by differing phonetic inventories of languages. In this paper, we propose that fast and effective CLSA can be made using vocal tract length normalization (VTLN), where strong constraints of the vocal tract warping function may actually help to avoid the most severe effects of the aforementioned mismatch. VTLN has a single parameter that warps spectrum. Using shifted or adapted pitch, VTLN can still achieve reasonable speaker similarity. We present our approach, VTLN-based CLSA, and evaluation results that support our proposal under the limitation that the voice identity and speaking style of a target speaker don’t diverge too far from that of the average voice model